538 IEEE TRANSACTIONS ON MOBILE COMPUTING, · PDF fileFriendbook: A Semantic-Based Friend Recommendation System for Social Networks Zhibo Wang, Student Member, IEEE, Jilong Liao, Qing

Friendbook: A Semantic-Based FriendRecommendation System for Social Networks

Zhibo Wang, Student Member, IEEE, Jilong Liao, Qing Cao,Member, IEEE,

Hairong Qi, Senior Member, IEEE, and Zhi Wang,Member, IEEE

Abstract—Existing social networking services recommend friends to users based on their social graphs, which may not be the most

appropriate to reflect a user’s preferences on friend selection in real life. In this paper, we present Friendbook, a novel semantic-based

friend recommendation system for social networks, which recommends friends to users based on their life styles instead of social

graphs. By taking advantage of sensor-rich smartphones, Friendbook discovers life styles of users from user-centric sensor data,

measures the similarity of life styles between users, and recommends friends to users if their life styles have high similarity. Inspired by

text mining, we model a user’s daily life as life documents, from which his/her life styles are extracted by using the Latent Dirichlet

Allocation algorithm. We further propose a similarity metric to measure the similarity of life styles between users, and calculate users’

impact in terms of life styles with a friend-matching graph. Upon receiving a request, Friendbook returns a list of people with highest

recommendation scores to the query user. Finally, Friendbook integrates a feedback mechanism to further improve the

recommendation accuracy. We have implemented Friendbook on the Android-based smartphones, and evaluated its performance on

both small-scale experiments and large-scale simulations. The results show that the recommendations accurately reflect the

preferences of users in choosing friends.

Index Terms—Friend recommendation, mobile sensing, social networks, life style

Ç

1 INTRODUCTION

TWENTY years ago, people typically made friends withothers who live or work close to themselves, such as

neighbors or colleagues. We call friends made through thistraditional fashion as G-friends, which stands for geograph-ical location-based friends because they are influenced bythe geographical distances between each other. With therapid advances in social networks, services such as Face-book, Twitter and Google+ have provided us revolutionaryways of making friends. According to Facebook statistics, auser has an average of 130 friends, perhaps larger than anyother time in history [2].

One challenge with existing social networking services ishow to recommend a good friend to a user. Most of themrely on pre-existing user relationships to pick friend candi-dates. For example, Facebook relies on a social link analysisamong those who already share common friends and recom-mends symmetrical users as potential friends. Unfortu-nately, this approach may not be the most appropriate based

on recent sociology findings [16], [27], [29], [30]. Accordingto these studies, the rules to group people together include:1) habits or life style; 2) attitudes; 3) tastes; 4) moral stand-ards; 5) economic level; and 6) people they already know.Apparently, rule #3 and rule #6 are the mainstream factorsconsidered by existing recommendation systems. Rule #1,although probably the most intuitive, is not widely usedbecause users’ life styles are difficult, if not impossible, tocapture through web actions. Rather, life styles are usuallyclosely correlated with daily routines and activities. There-fore, if we could gather information on users’ daily routinesand activities, we can exploit rule #1 and recommendfriends to people based on their similar life styles. This rec-ommendation mechanism can be deployed as a standaloneapp on smartphones or as an add-on to existing social net-work frameworks. In both cases, Friendbook can helpmobilephone users find friends either among strangers or within acertain group as long as they share similar life styles.

In our everyday lives, we may have hundreds of activi-ties, which form meaningful sequences that shape our lives.In this paper, we use the word activity to specifically refer tothe actions taken in the order of seconds, such as “sitting”,“walking”, or “typing”, while we use the phrase life style torefer to higher-level abstractions of daily lives, such as“office work” or “shopping”. For instance, the “shopping”life style mostly consists of the “walking” activity, but mayalso contain the “standing” or the “sitting” activities.

To model daily lives properly, we draw an analogybetween people’s daily lives and documents, as shown inFig. 1. Previous research on probabilistic topic models intext mining has treated documents as mixtures of topics,and topics as mixtures of words [10]. Inspired by this, simi-larly, we can treat our daily lives (or life documents) as a

� Z. Wang is with the Key Laboratory of Aerospace Information Security andTrusted Computing, School of Computer, Wuhan University, China,430072, and Department of Electrical Engineering and Computer Science,University of Tennessee, Knoxville, TN 37909.E-mail: [email protected].

� J. Liao, Q. Cao, andH. Qi are with the Department of Electrical Engineeringand Computer Science, University of Tennessee, Knoxville, TN 37909.E-mail: {jliao2, cao, hqi}@utk.edu.

� Z. Wang is with the State Key Laboratory of Industrial Control Technol-ogy, Zhejiang University, Hangzhou 310027, P.R. China.E-mail: [email protected].

Manuscript received 21 Jan. 2014; revised 21 Apr. 2014; accepted 26 Apr.2014. Date of publication 6 May 2014; date of current version 29 Jan. 2015.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference the Digital Object Identifier below.Digital Object Identifier no. 10.1109/TMC.2014.2322373

538 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 14, NO. 3, MARCH 2015

1536-1233� 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

mixture of life styles (or topics), and each life style as a mix-ture of activities (or words). Observe here, essentially, werepresent daily lives with “life documents”, whose semanticmeanings are reflected through their topics, which are lifestyles in our study. Just like words serve as the basis ofdocuments, people’s activities naturally serve as the primi-tive vocabulary of these life documents.

Our proposed solution is also motivated by the recentadvances in smartphones, which have become more andmore popular in people’s lives. These smartphones (e.g.,iPhone or Android-based smartphones) are equipped witha rich set of embedded sensors, such as GPS, accelerometer,microphone, gyroscope, and camera. Thus, a smartphone isno longer simply a communication device, but also a power-ful and environmental reality sensing platform from whichwe can extract rich context and content-aware information.From this perspective, smartphones serve as the ideal plat-form for sensing daily routines from which people’s lifestyles could be discovered.

In spite of the powerful sensing capabilities of smart-phones, there are still multiple challenges for extracting users’life styles and recommending potential friends based on theirsimilarities. First, how to automatically and accurately dis-cover life styles from noisy and heterogeneous sensor data?Second, how tomeasure the similarity of users in terms of lifestyles? Third, who should be recommended to the useramong all the friend candidates? To address these challenges,in this paper, we present Friendbook, a semantic-based friendrecommendation system based on sensor-rich smartphones.The contributions of this work are summarized as follows:

� To the best of our knowledge, Friendbook is the firstfriend recommendation system exploiting a user’s lifestyle informationdiscovered from smartphone sensors.

� Inspired by achievements in the field of text mining,we model the daily lives of users as life documentsand use the probabilistic topic model to extract lifestyle information of users.

� We propose a unique similarity metric to character-ize the similarity of users in terms of life styles andthen construct a friend-matching graph to recom-mend friends to users based on their life styles.

� We integrate a linear feedback mechanism thatexploits the user’s feedback to improve recommen-dation accuracy.

� We conduct both small-scale experiments and large-scale simulations to evaluate the performance of oursystem. Experimental results demonstrate the effec-tiveness of our system.

The rest of the paper is organized as follows. Section 2discusses related work. Section 3 provides the high-leveloverview of Friendbook. Section 4 presents activity recog-nition and life style modeling and extraction. In Section 5,we describe the social graph construction and user impactestimation. We elaborate on the user query and friend rec-ommendation in Section 6. We describe the feedbackmechanism in Section 7. In Section 8, we evaluate the per-formance of Friendbook intensively with both simulationsand real experiments. Finally, we conclude the paper andpresent the future work in Section 9.

2 RELATED WORK

Recommendation systems that try to suggest items (e.g.,music, movie, and books) to users have become more andmore popular in recent years. For instance, Amazon [1] rec-ommends items to a user based on items the user previ-ously visited, and items that other users are looking at.Netflix [3] and Rotten Tomatoes [4] recommend movies to auser based on the user’s previous ratings and watchinghabits. Recently, with the advance of social networking sys-tems, friend recommendation has received a lot of atten-tion. Generally speaking, existing friend recommendationin social networking systems, e.g., Facebook, LinkedIn andTwitter, recommend friends to users if, according to theirsocial relations, they share common friends.

Meanwhile, other recommendation mechanisms havealso been proposed by researchers. For example, Bian andHoltzman [8] presented MatchMaker, a collaborative filter-ing friend recommendation system based on personalitymatching. Kwon and Kim [20] proposed a friend recom-mendation method using physical and social context. How-ever, the authors did not explain what the physical andsocial context is and how to obtain the information. Yu et al.[32] recommended geographically related friends in socialnetwork by combining GPS information and social networkstructure. Hsu et al. [18] studied the problem of link recom-mendation in weblogs and similar social networks, and pro-posed an approach based on collaborative recommendationusing the link structure of a social network and content-based recommendation using mutual declared interests.Gou et al. [17] proposed a visual system, SFViz, to supportusers to explore and find friends interactively under thecontext of interest, and reported a case study using thesystem to explore the recommendation of friends based onpeople’s tagging behaviors in a music community. Theseexisting friend recommendation systems, however, are sig-nificantly different from our work, as we exploit recent soci-ology findings to recommend friends based on their similarlife styles instead of social relations.

Activity recognition serves as the basis for extractinghigh-level daily routines (in close correlation with lifestyles) from low-level sensor data, which has been widelystudied using various types of wearable sensors. Zhenget al. [33] used GPS data to understand the transportationmode of users. Lester et al. [21] used data from wearablesensors to recognize activities based on the Hidden MarkovModel (HMM). Li et al. [22] recognized static postures anddynamic transitions by using accelerometers and gyro-scopes. The advance of smartphones enables activity

Fig. 1. An analogy between word documents and people’s daily lives.

WANG ET AL.: FRIENDBOOK: A SEMANTIC-BASED FRIEND RECOMMENDATION SYSTEM FOR SOCIAL NETWORKS 539

recognition using the rich set of sensors on the smartphones.Reddy et al. [26] used the built-in GPS and the accelerome-ter on the smartphones to detect the transportation mode ofan individual. CenceMe [24] used multiple sensors on thesmartphone to capture user’s activities, state, habits andsurroundings. SoundSense [23] used the microphone on thesmartphone to recognize general sound types (e.g., music,voice) and discover user specific sound events. EasyTracker[7] used GPS traces collected from smartphones that areinstalled on transit vehicles to determine routes served,locate stops, and infer schedules.

Although a lot of work has been done for activity recog-nition using smartphones, there is relatively little work ondiscovery of daily routines using smartphones. The MITReality Mining project [12] and Farrahi and Gatica-Perez[14] tried to discover daily location-driven routines fromlarge-scale location data. They could infer daily routinessuch as leaving from home to office and eating at a restau-rant. However, they could not discover the daily routines ofpeople who are staying at the same location. For instance,when one stays at home, his/her daily routines like “eatinglunch” and “watching movie” could not be discovered ifonly using the location information. In [13], Farrahi andGatica-Perez took a step further and overcame the short-coming of discovering daily routines of people staying inthe same location by considering combined location andphysical proximity sensed by the mobile phone. Anotherclosely related work was presented in [19], which used atopic model to extract activity patterns from sensor data.However, they used two wearable sensors, but not smart-phones, to discover the daily routines. In our work, weattempt to use the probabilistic topic model to discover lifestyles using the smartphone. We further utilize patterns dis-covered from activities as a basis for friend recommenda-tion that helps users find friends who have similar lifestyles. Note that the work in this paper is significantly dif-ferent from our preliminary demo work of Friendbook [31]that recommended friends to users based on the similarityof pictures taken by users.

3 SYSTEM OVERVIEW

In this section, we give a high-level overview of the Friend-book system. Fig. 2 shows the system architecture of

Friendbook which adopts a client-server mode where eachclient is a smartphone carried by a user and the servers aredata centers or clouds.

On the client side, each smartphone can record data of itsuser, perform real-time activity recognition and report thegenerated life documents to the servers. It is worth notingthat an offline data collection and training phase is neededto build an appropriate activity classifier for real-time activ-ity recognition on smartphones. We spent three months oncollecting raw data of eight volunteers for building a largetraining data set. As each user typically generates around50 MB of raw data each day, we choose MySQL as our lowlevel data storage platform and Hadoop MapReduce as ourcomputation infrastructure. After the activity classifier isbuilt, it will be distributed to each user’s smartphone andthen activity recognition can be performed in real-timemanner. As a user continually uses Friendbook, he/she willaccumulate more and more activities in his/her life docu-ments, based on which, we can discover his/her life stylesusing probabilistic topic model.

On the server side, seven modules are designed to fulfillthe task of friend recommendation. The data collection mod-ule collects life documents from users’ smartphones. Thelife styles of users are extracted by the life style analysis mod-ule with the probabilistic topic model. Then the life styleindexing module puts the life styles of users into the data-base in the format of (life-style, user) instead of (user, life-style). A friend-matching graph can be constructed accord-ingly by the friend-matching graph construction module torepresent the similarity relationship between users’ lifestyles. The impacts of users are then calculated based on thefriend-matching graph by the user impact ranking module.The user query module takes a user’s query and sends aranked list of potential friends to the user as response. Thesystem also allows users to give feedback of the recommen-dation results which can be processed by the feedback controlmodule. With this module, the accuracy of friend recom-mendation can be improved.

In the following sections, we will elaborate on all thecomponents of the system.

4 LIFE STYLE EXTRACTION USING TOPIC MODEL

4.1 Life Style Modeling

As stated in Section 1, life styles and activities are reflectionsof daily lives at two different levels where daily lives can betreated as a mixture of life styles and life styles as a mixtureof activities. This is analogous to the treatment of docu-ments as ensemble of topics and topics as ensemble ofwords. By taking advantage of recent developments in thefield of text mining, we model the daily lives of users as lifedocuments, the life styles as topics, and the activities as words.

Given “documents”, the probabilistic topic model coulddiscover the probabilities of underlying “topics”. Therefore,we adopt the probabilistic topic model to discover the prob-abilities of hidden “life styles” from the “life documents”.In probabilistic topic models, the frequency of vocabulary isparticularly important, as different frequency of wordsdenotes their information entropy variances. Following thisobservation, we propose the “bag-of-activity” model (Fig. 3)to replace the original sequences of activities recognized

Fig. 2. System architecture of Friendbook.


based on the raw data with their probability distributions.Thereafter, each user has a bag-of-activity representation ofhis/her life document, which comprises a mixture of activ-ity words.

Let w ¼ ½w1; w2; . . . ; wW � denote a set of activities, wherewi is the ith activity and W is the total number of activities.Let z ¼ ½z1; z2; . . . ; zZ � denote a set of life styles, where zi isthe ith life style and Z is the total number of life styles. Letd ¼ ½d1; d2; . . . ; dn� denote a set of life documents, where diis the ith life document and n is the total number of users.Let pðwi j dkÞ denote the probability of the activity wi in acertain life document dk, pðwi j zjÞ denote the probability ofhow much the activity wi contributes to the life style zj, andpðzj j dkÞ denote the probability of the life style zj embeddedin the life document dk. According to the probabilistic topicmodel, we have

pðwi j dkÞ ¼XZj¼1

pðwi j zjÞpðzj j dkÞ: (1)

Observe that pðwi j dkÞ can be easily calculated by usingthe “bag-of-activity” representation for the life document dk:

pðwi j dkÞ ¼ fkðwiÞPWi¼1 fkðwiÞ

; (2)

where fkðwiÞ denotes the frequency of wi in dk.We represent the life styles of a user using the life style

vector, denoted by Lk ¼ ½pðz1 j dkÞ; pðz2 j dkÞ; . . . ; pðzZ j dkÞ�. Inthis paper, our objective is to discover the life style vectorfor each user given the life documents of all users. However,in Eq. (1), although pðwi j dkÞ can be calculated easily,pðwi j zjÞ and pðzj j dkÞ are difficult to solve because of thehidden feature of life styles.

In the following sections, we first present the details ofactivity recognition used to calculate pðwi j dkÞ, then showhow to use the Latent Dirichlet Allocation (LDA) decompo-sition algorithm to solve Eq. (1) so that we can obtain thelife style vector of each user.

4.2 Activity Recognition

To derive pðwi j dkÞ, we need to first classify or recognize theactivities of users. Life styles are usually reflected as a mix-ture of motion activities with different occurrence probabil-ity. Therefore, two motion sensors, accelerometer and

gyroscope, are used to infer users’ motion activities. Gener-ally speaking, there are two mainstream approaches: super-vised learning and unsupervised learning. For bothapproaches, mature techniques have been developed andtested. In practice, the number of activities involved in theanalysis is unpredictable and it is difficult to collect a largeset of ground truth data for each activity, which makessupervised learning algorithms unsuitable for our system.Therefore, we use unsupervised learning approaches to rec-ognize activities. Here, we adopt the popular K-means clus-tering algorithm [9] to group data into clusters, where eachcluster represents an activity. Note that activity recognitionis not the main concern of our paper. Other more compli-cated clustering algorithms can certainly be used. Wechoose K-means for its simplicity and effectiveness.

Fig. 4 shows the flowchart of activity recognition. Sincethe raw data collected on the smartphones are noisy, wefirst use a median filter [5] with sliding windows to filterout the outliers of the noisy data. In order to furtherimprove recognition accuracy, features are extracted tocharacterize the data after preprocessing. We have testedseveral features, such as mean, standard deviation, correla-tion, and the combination of them, on the data and foundthat standard deviation is the most representative featurefor characterizing motion activities. Therefore, a feature vec-tor f ¼ ½tc; accx; accy; accz; gyrx; gyry; gyrz� is used to charac-terize user’s activities instead of the raw data, where tcis the mean of normalized time; accx, accy and accz representthe standard deviation of normalized data obtained fromthe accelerometer in the x, y and z directions, respectively;gyrx, gyry and gyrz represent the standard deviation of nor-malized data obtained from the gyroscope in the x, y and zdirections, respectively. We then apply the K-means cluster-ing algorithm on the feature vectors to group them into dif-ferent clusters as well as calculating the cluster centroids.Our empirical study in Section 8.1.1 indicates that K ¼ 15 isa good compromise between classification accuracy andcomputational time. The cluster centroids are then distrib-uted to the smartphones. Then each smartphone could inde-pendently recognize activities based on the minimumdistance rule and upload the activity sequence instead ofthe raw data to the server.

Fig. 3. Bag-of-activity modeling for life document.

Fig. 4. The flowchart of activity recognition.


4.3 Life Style Extraction Using LDA

Given the life documents of all users, Eq. (1) can be furtherrepresented as a matrix decomposition problem:

pðw jdÞ ¼ pðw j zÞpðz jdÞ; (3)

where pðw jdÞ ¼ ½pðw j d1Þ; pðw j d2Þ; . . . ; pðw j dnÞ� is theactivity-document matrix as shown in Fig. 5 containing theprobability of each activity over each life document, andpðw j dkÞ ¼ ½pðw1 j dkÞ; pðw2 j dkÞ; . . . ; pðwW j dkÞ�T is the kthcolumn in the activity-document matrix representing theprobabilities of activities over the life document dk of user k;pðw j zÞ ¼ ½pðw j z1Þ; pðw j z2Þ; . . . ; pðw j zZÞ� is the activity-topic matrix as shown in Fig. 5 representing the probabilityof each activity over each life style (topic), and pðw j zkÞ ¼½pðw1 j zkÞ; pðw2 j zkÞ; . . . ; pðwW j zkÞ�T is the kth column in theactivity-topic matrix representing the probabilities of activi-ties over the life style zk; pðz jdÞ ¼ ½pðz j d1Þ; pðz j d2Þ; . . . ;pðz j dnÞ� is the topic-document matrix as shown in Fig. 5containing the probability of each topic over each life docu-ment, and pðz j dkÞ ¼ ½pðz1 j dkÞ; pðz2 j dkÞ; . . . ; pðzZ j dkÞ�T isthe kth column in the topic-document matrix representingthe probabilities of life styles over the life document dk ofuser k.

The above matrix decomposition problem is actually theLatent Dirichlet Allocation model [10]. We use the Expecta-tion-Maximization (EM) method to solve the LDA decompo-sition, where the E-step is used to estimate the freevariational Dirichlet parameter g [15] and multinomialparameter F in the standard LDA model [10] and the M-step is used to maximize the log likelihood of the activitiesunder these parameters. After the EM algorithm converges,we are able to calculate the decomposed activity-topicmatrix. Readers are referred to [10] for more details of theLDA algorithm and alternative decomposition approaches.It is worth noting that the matrix decomposition processcan be implemented more efficiently through incrementaliteration. That is, when a user’s life document changes or anew user’s life document is uploaded to the system, Friend-book can calculate the new life style vectors for each userbased on previously derived life style vectors and the newlife document. We did not implement the incremental itera-tion in the current framework by simply replying on thecomputing power of cloud computing. As part of our futurework, we could add this implementation to the frameworkto make Friendbook scalable to large-scale systems.

It is also worth noting that since our system uses unsu-pervised learning algorithms to recognize activities andthe topic model to discover life styles, the physical mean-ings of derived “activities” (or cluster centers from the K-means algorithm) or “topics” are unknown to us. As men-tioned in [19], such meaning can be estimated via the addi-tional step of comparing the topic activations to the actualstructure of the subject’s day and then identifying topics

that correspond to possible daily routines. In Friendbook,since we are to only compare “similarity” in activities ortopic patterns, there is no need to infer the physical mean-ing of each cluster center or topic. On the other hand, notrevealing the actual physical meaning of activities andtopics also has advantages from the perspective of pre-serving privacy.

5 FRIEND-MATCHING GRAPH AND USER IMPACT

To characterize relations among users, in this section, wepropose the friend-matching graph to represent the similaritybetween their life styles and how they influence other peo-ple in the graph. In particular, we use the link weightbetween two users to represent the similarity of their lifestyles. Based on the friend-matching graph, we can obtain auser’s affinity reflecting how likely this user will be chosenas another user’s friend in the network.

5.1 Similarity Metric

We define a new similarity metric to measure the similaritybetween two life style vectors. Let Li ¼ ½pðz1 j diÞ; pðz2 j diÞ;. . . ; pðzZ j diÞ� and Lj ¼ ½pðz1 j djÞ; pðz2 j djÞ; . . . ; pðzZ j djÞ�denote the life style vectors of user i and user j, respectively.

We argue that the similarity is not only affected by theirlife style vectors as a whole, but also by the most importantlife styles, i.e., the elements within the vector with largerprobability values, also known as the dominant life styles.We also argue that two users do not share much similarityif majority of their life styles are totally different. Therefore,the similarity of life styles between user i and user j,denoted by Sði; jÞ, is defined as follows:

Sði; jÞ ¼ Scði; jÞ � Sdði; jÞ; (4)

where Scði; jÞ is used to measure the similarity of the lifestyle vectors of users as a whole, Sdði; jÞ is used to empha-size the similarity of users on their dominant life styles.

We adopt the commonly used cosine similarity metric forScði; jÞ, that is,

Scði; jÞ ¼ cosðLi;LjÞ: (5)

In order to calculate Sdði; jÞ, we first define the set ofdominant life styles of a user.

Definition 1 (Dominant life styles). The set of dominant lifestyles of a user i, Di, is a subset of all the life styles satisfyingthe following requirements:

1) The total probability distribution of the set is largerthan or equal to � which is a predefined threshold.

2) The probability distribution of any life style in the set islarger than or equal to that of any life style not in theset.

3) The set should have the minimum number of life styles.

These requirements guarantee that life styles with largerprobabilities are more probably to be included in the set. Tofind Di, we sort the life style vector Li in the descendingorder with respect to the probabilities of life styles. Then wehave L̂i ¼ ½pðzi1 j diÞ; pðzi2 j diÞ; . . . ; pðziZ j diÞ� where pðzis jdiÞ � pðzit j diÞ if s � t. The size of dominant life style set iscalculated as

Fig. 5. Matrix decomposition for life styles analysis. (Redrawn from [19]).


qi ¼ argminq

Xqk¼1

pðzik j diÞ � �

!: (6)

Finally, we can obtain the dominant life style setDi ¼ fzi1; . . . ; ziqig.

The similarity metric Sdði; jÞ for measuring the similarityof the dominant life style sets of two users is then defined as

Sdði; jÞ ¼ 2 jDi \DjjjDij þ jDjj : (7)

The range of Sdði; jÞ is ½0; 1�. Observe that the higher thepercentage of the same life styles, the larger the similarity.When there is no overlap between Di and Dj, the similarityis 0. When Di and Dj are the same, the similarity is 1. Sinceboth Scði; jÞ and Sdði; jÞ vary between 0 and 1, we concludethat the similarity metric Sði; jÞ varies between 0 and 1.

As an example to show the calculation of two users’ lifestyle similarity, we assume that there are two users 1 and 2in the system, who have the life style vectors L1 ¼½0:3; 0:1; 0:2; 0:3; 0:1� and L2 ¼ ½0:2; 0:1; 0:4; 0; 0:3�, respec-tively. The number of life style topics is 5. We first calculateScð1; 2Þ ¼ cosðL1;L2Þ ¼ 0:6708. Given � ¼ 0:8, we can calcu-late the dominant life style sets of these two users,D1 ¼ fz1; z4; z3g and D2 ¼ fz3; z5; z1g, respectively. There-fore, the dominant life style similarity is calculated asSdð1; 2Þ ¼ 2 � 2

3 þ 3 ¼ 0:67. Finally, the similarity of user 1 and 2is Sð1; 2Þ ¼ Scð1; 2Þ � Sdð1; 2Þ ¼ 0:45.

5.2 Friend-Matching Graph Construction

Based on the similarity metric, we model the relationsbetween users in real life as a friend-matching graph.

Definition 2 (Friend-matching graph). It is a weighted undi-rected graph G ¼ ðV;E;WÞ, where V ¼ fv1; v2; . . . ; vng is theset of users and n is the number of users, E ¼ feði; jÞg is theset of links between users, andW : E ! R is the set of weightsof edges. There is an edge eði; jÞ linking user i and user j if andonly if their similarity Sði; jÞ � Sthr, where Sthr is the prede-fined similarity threshold. The weight of that edge is repre-sented by the similarity, that is, vði; jÞ ¼ Sði; jÞ.Fig. 6 demonstrates a friend-matching graph based on the

life styles of eight users and the similarity threshold Sthr isset to 0.3. An edge linking two users means they have similarlife styles (e.g., eð1; 7Þ), and their similarity is quantified bythe weight of the edge (e.g., vð1; 7Þ ¼ 0:62). Some isolated

vertices mean that they do not share enough similar lifestyles with others (e.g., user 4). We use the following repre-sentation to convert the graph into amatrix representation:

N ¼ ðNijÞn�n ¼0 vð1; 2Þ � � � vð1; nÞ

vð2; 1Þ 0 � � � vð2; nÞ... ..

. . .. ..

.

vðn; 1Þ vðn; 2Þ � � � 0

26664

37775: (8)

Note that the values on the diagonal are all 0 because wewould not recommend himself/herself to a user.

5.3 User Impact Ranking

The friend-matching graph has been constructed to reflectlife style relations among users. However, we still lack ameasurement to identify the impact ranking of a user quan-titatively. Intuitively, the impact ranking means a user’scapability to establish friendships in the network. In otherwords, the higher the ranking, the easier the user can bemade friends with, because he/she shares broader life styleswith others. Inspired by PageRank [25] which is used inweb page ranking, we form the idea that a user’s ranking isreflected by his neighbors in the friend-matching graph andhowmuch his neighbors endorse the user as a friend.

Once the ranking of a user is obtained, it provides guide-lines to those who receive the recommendation list on howto choose friends. The ranking itself, however, should beindependent from the query user. In other words, the rank-ing depends only on the graph structure of the friend-matching graph, which contains two aspects: 1) how theedges are connected; 2) how much weight there is on everyedge. Moreover, the ranking should be used together withthe similarity scores between the query user and the poten-tial friend candidates, so that the recommended friends arethose who not only share sufficient similarity with the queryuser, and are also popular ones through whom the queryuser can increase their own impact rankings.

Let NðiÞ denote the set of neighbors of user i. Letr ¼ ½rð1Þ; rð2Þ; . . . ; rðnÞ�T denote the impact ranking vectorwhere rðiÞ is the impact ranking of user i in the friend-matching graph, and n is the number of users in the system.The calculation of rðiÞ is defined as follows:

rðiÞ ¼P

j2NðiÞ vði; jÞ � rðjÞPj2NðiÞ vði; jÞ

: (9)

As shown in Eq. (9), the impact ranking of a user isaffected by its neighbors from two aspects: first, the similar-ity between itself and a neighbor; second, the impact rank-ing of its neighbors. Note that in friend-matching graph,vði; jÞ ¼ 0 if j is not a neighbor of i. Also vði; iÞ ¼ 0 becausewe would not recommend himself/herself to a user. There-fore, Eq. (9) can be rewritten as follows:

rðiÞ ¼P

j vði; jÞ � rðjÞPj vði; jÞ

: (10)

The calculation of rðiÞ is an iterative process because anychange of its neighbors will change rðiÞ accordingly. There-fore, we use a matrix representation to clearly show the iter-ative process of Eq. (10):

Fig. 6. An example of friend-matching graph for eight users.


rTkþ1 ¼ rTk �H; (11)

where k and kþ 1 indicate two subsequent iteration steps,and H ¼ ðHijÞn�n is the transitional matrix representing thesimilarity of neighbors. Combining Eq. (8) and Eq. (10), anelementHij inH is calculated as follows:

Hij ¼ vði; jÞPj vði; jÞ

¼ NijPj Nij

: (12)

In practice, it is possible that people choose friends ran-domly rather than based on their importance. Therefore, werevise the transitional matrix by introducing a matrix withequal value. Then the transitionalmatrix ismodified to Eq. (13):

~H ¼ ’Hþ ð1� ’Þ 1neeT ; (13)

where e is then� 1 unit vector and ’ is the damping factor usedto emphasize the importance of the friend-matching graph.

Finally, the iterative process for calculating the impactrank of users is carried out as follows:

rTkþ1 ¼ rTk � ~H: (14)

The process terminates when the impact ranking vectorconverges to a stable value. In our experiment, the processterminates when

Pni¼1 jrkþ1ðiÞ � rkðiÞj � " where " is an

arbitrary small value larger than 0. The pseudocode for theiterative process is shown in Algorithm 1. After the impactvector r is calculated, we can rank the users in a non-descending order so that a user with higher impact rankingis always ahead of a user with lower impact ranking.

In general, by using the Hadoop MapReduce framework,the impact ranking process can converge quickly. However,this is becoming increasing infeasiblewhen the size of the sys-tem is becoming very large. Fortunately, accordingly to theincremental computation of PageRank [6], [11] and the dis-tributed computation of PageRank [28], the iterative matrix-vector multiplication method in Eq. (14) can be implementedincrementally or distributively for large-scale evolvinggraphs. In [28], the authors presented a fast algorithm thattakes Oð ffiffiffiffiffiffiffiffiffiffiffi

log np

=�Þ rounds in undirected graphs, where n isthe network size and � is a fixed constant. Therefore,

Friendbook is scalable to large-scale systems if we couldimplement the iterative matrix-vector multiplication methodincrementally or distributively, which would be our futurework.

6 QUERY AND FRIEND RECOMMENDATION

Before a user initiates a request, he/she should have accu-mulated enough activities in his/her life documents for effi-cient life styles analysis. The period for collecting datausually takes at least one day. Longer time would beexpected if the user wants to get more satisfied friend rec-ommendation results. After receiving a user’s request (e.g.,life documents), the server would extract the user’s life stylevector, and based on which recommend friends to the user.

The recommendation results are highly dependent onusers’ preference. Some users may prefer the system to rec-ommend users with high impact, while some users maywant to know users with the most similar life styles. It is alsopossible that some users want the system to recommendusers who have high impact and also similar life styles tothem. To better characterize this requirement, we proposethe followingmetric to facilitate the recommendation:

RiðjÞ ¼ bSði; jÞ þ ð1� bÞrjk; (15)

where RiðjÞ is the recommendation score of user j for thequery user i, Sði; jÞ is the similarity between user i anduser j, and rj is the impact of user j. b 2 ½0; 1� is the recom-mendation coefficient characterizing users’ preference. k isintroduced to make Sði; jÞ and rj in the same order of mag-nitude, which can be roughly set to n=10, where n is thenumber of users in the system. When b ¼ 1, the recom-mendation is solely based on the similarity; when b ¼ 0,the recommendation is solely based on the impact ranking.

With the metric in Eq. (15), our recommendation mecha-nism for finding the most appropriate friends to a queryuser is described as follows. For a query user i, the servercalculates the recommendation scores for all the users in thesystem and sorts them in the descending order according totheir recommendation scores. The top p users will bereturned to the query user i. The parameter p is an integerand can be defined by the querying user. The complexity ofour recommendation mechanism is OðnÞ since it checks allusers in the system, where n is the overall number of usersin the system.

As the number of users increases, the overhead of queryand recommendation increases linearly. In reality, usersmay have totally different life styles and it is not necessaryto calculate their recommendation scores at all. Therefore,in order to speed up the query and recommendation pro-cess, we adopt the reverse index table using (life-style, user)pair instead of (user, life-style) pair in the database. Fig. 7shows the difference. With the reverse index table, beforecalculating recommendation score for each user, the serverfirst picks up all the users having overlapping life styleswith the query user and sets the similarities of rest users tothe query user to 0. The server then checks all the users tocalculate their recommendation scores. Although the com-plexity is still OðnÞ, we can observe that the reverse indextable reduces the computation overhead, the advantage ofwhich is considerable when the system is in large-scale.


The pseudocode of the friend recommendation mecha-nism is shown in Algorithm 2.

Friendbook also uses GPS location information to helpusers find friends within some distance. In order to protectthe privacy of users, a region surrounding the accurate loca-tion will be uploaded to the system. When a user usesFriendbook, he/she can specify the distance of friendsbefore recommendation. In this way, only friends havingsimilarity with the user within the specified distance can berecommended as friends.

Privacy is very important especially for users who aresensitive to information leakage. In our design of Friend-book, we also considered the privacy issue and the existingsystem can provide two levels of privacy protection. First,Friendbook protects users’ privacy at the data level. Instead

of uploading raw data to the servers, Friendbook processesraw data and classifies them into activities in real-time. Therecognized activities are labeled by integers. In this way,even if the documents containing the integers are compro-mised, they cannot tell the physical meaning of the docu-ments. Second, Friendbook protects users’ privacy at the lifepattern level. Instead of telling the similar life styles ofusers, Friendbook only shows the recommendation scoresof the recommended friends with the users. With the recom-mendation score, it is almost impossible to infer the lifestyles of recommended friends.

7 FEEDBACK CONTROL

To support performance optimization at runtime, we alsointegrate a feedback control mechanism into Friendbook.After the server generates a reply in response to a query, thefeedback mechanism allows us to measure the satisfaction ofusers, by providing a user interface that allows the user to ratethe friend list. Let �r denote the impact ranking vector calcu-lated from the feedback of users. Here, �r ¼ ½�rð1Þ; �rð2Þ; . . . ;�rðnÞ�T where n is the number of current users of the system.Let �rði; jÞ denote the score that user j rates user i. Then wehave:

�rði; jÞ ¼ �rði; jÞ if user j rates user i;rðiÞ otherwise,

�(16)

where the second equation in Eq. (16) means that the feed-back score is equal to the original score if user j does notrate user i. This may commonly occur when the user doesnot know the persons being recommended especially whenour system becomes very large.

Based on Eq. (16), we define the impact ranking of user iinfluenced by the feedback of users as follows:

�rðiÞ ¼Xj

�rði; jÞ=n; (17)

which takes the feedback of all users into consideration.Finally, the original impact ranking vector R calculated

from the friend-matching graph is updated as follows:

r ¼ arþ ð1� aÞ�r; (18)

where a is named as confidence factor and 0 � a � 1. Thefinal impact ranking vector considers both the influence offriend-matching graph and the feedback from users. Whena > 0:5, the friend-matching graph dominates the impactranking, however, when a < 0:5, users’ feedback will sig-nificantly affect the impact ranking. In this way, the systemtakes users’ feedback into consideration to improve theaccuracy of future recommendations.

8 EVALUATION

In this section, we present the performance evaluation ofFriendbook on both small-scale field experiments and large-scale simulations.

8.1 Evaluation Using Real Data

We first evaluate the performance of Friendbook on small-scale experiments. Eight volunteers help contribute data

Fig. 7. Illustration of the reverse index table.


and evaluate our system. Table 1 demonstrates the profes-sion of these users. Most of them are students, while therest include a businessman, an office worker, and a wait-ress. Each volunteer carries a Nexus S smartphone withFriendbook application installed in advance.

They are required to start the application after theywake up and turn it off before they go to bed. Aside fromthis, we do not impose any additional requirement on theusage of the smartphone. For example, we do not requirethem to carry the smartphone all the time during the day orattach the smartphone to some special parts of the body.

It is worth noting that some of the eight users are alreadyfriends before experiments but some of them are not. Infact, some strangers within the group become friends after-wards. However, strangers living far away from each otherdo not become friends although they choose each other as afriend at the friend recommendation phase. This also moti-vates the usage of GPS information into the system toimprove the recommendation accuracy.

In the following, we first present the activity classifica-tion results using K-means clustering algorithm, and thenshow the performance evaluation on real experiments.

8.1.1 Activity Classification

Fig. 8 shows the classification results using the K-meansclustering algorithm on the data collected from the eightusers for a period of three months. Feature vectors insteadof raw data are used for classification. Each feature vectorconsists of seven attributes, f ¼ ½tc; accx; accy; accz; gyrx; gyry;gyrz� (see Section 4.2), and we extract feature vectors every60 seconds. As shown in Fig. 8a, the sum of squared errordrops quickly when the cluster number K increases from 1to 10, and then does not change too much after 15. Thisimplies that the daily lives of the volunteers are roughlycomposed of 15 different types of activities. Although wecan use additional activities to characterize their daily livesat a finer scale, we find 15 an appropriate compromise asthe most important activities are already involved. Otheractivities occur rarely and cannot considerably affect thefriend recommendation results. Therefore, we use K ¼ 15as the number of activities in Friendbook.

Fig. 8b demonstrates the distribution of 15 activities. Mostof activities have the probability of about 6 percent, and threeactivities have the probability of larger than 10 percent, andthe remaining three activities have the probability of less than2 percent. Corresponding to each activity, a centroid featurevector is calculated by using the K-means clustering algo-rithm. These 15 centroids are distributed to each smartphoneso that it can perform real-time onboard activity recognition.

8.1.2 Friend Recommendation Results

There are four free parameters used to generate the friendrecommendation results, including the similarity thresholdfor friend-matching graph Sthr (Definition 2), the threshold� (Eq. (6)) that controls the number of dominant life styles,the damping factor ’ that emphasizes the importance of thefriend matching graph (Eq. (13)), and the number of lifestyles. In our practical experiments, we have used the fol-lowing values as default through empirical studies, i.e., thesimilarity threshold Sthr is set to 0:5, the threshold � is set to0:8, the damping factor ’ is set to 0:85, and the number oflife styles is set to 10.

Fig. 9 shows several user interfaces of Friendbook. Fig. 9ashows a snapshot of the query and recommendation user

TABLE 1Profession of Users

Fig. 8. Classification performance using the K-means clustering.

Fig. 9. User interfaces: (a) query-recommendation interface; (b) connec-tion interface; (c) rating interface.


interface. The IDs and recommendation scores of recom-mended friends are shown in the list. Note that Friendbookreturns the ID of users instead of their real names due to pri-vacy concerns in our experiments. Figs. 9b and 9c show thesnapshots of user feedback interfaces. Users can connect topeople in the recommended friend list through our systemand also give a score on the recommended friends. Notethat we intentionally anonymize the personal informationin Fig. 9 to protect the privacy of subjects. In the real system,when a user wants to use the system, he/she will be encour-aged to complete his/her personal profile, e.g., name andphoto. Therefore, the name and photo information as wellas the similarity score of each recommended friend will beshown to the user.

Fig. 10 illustrates the gray-scale image representation ofthe similarity matrix for eight users. The blocks in the diago-nal has the darkest color because users always have the per-fect match with themselves. As shown in Fig. 10, user 1 hasstrong relationship with user 2 and user 5, user 3 has strongrelationship with user 7, user 6 has relationship with theaforementioned users but not very strong, while user 4 anduser 8 have no relationship with others at all. The result isconsistent with the ground truth of professions shown inTable 1 because people have the same profession usuallyhave the same life styles.

The user impact rankings of the eight users are shownin Table 2. The top ranks are users 1 and 7, followed byusers 4 and 8 who seem to have high impact ranks. How-ever, users 4 and 8 are not supposed to be higher thanothers because they have no connections. Indeed, becauseof this, they should always maintain the initial score.Since we only have eight users in the system, each ofwhom uses 1

8 ¼ 0:125 as its initial random impact, asdescribed in Algorithm 1, which results in that their rank-ings are even higher than some of the connected users. Ifwe have 1;000 users, they should only have 0:001 for theirfinal score, thus they would not have so high rank.Although the independent users are not avoidable, weonly recommend a small portion of top p friends. Thechance that an independent user ranks in top p is smallwhen the total number of users is large. Therefore, therecommendation quality should not be affected muchfrom independent users.

8.2 Evaluation Using Simulated Data

We perform simulations to further evaluate performance ofFriendbook when the scale of the system is large. Our friendrecommendation method is based on life styles extractedfrom sensors on users’ smartphones, which is quite differentfrom existing friend recommendation methods. To the bestof our knowledge, there is no real data set that can be usedfor a large-scale performance evaluation.

In our simulation, we randomly and independently gen-erate the life style vectors for 1,000 users, Lk ¼ ½pðz1 j dkÞ;pðz2 j dkÞ; . . . ; pðz10 j dkÞ�; k ¼ 1; . . . ; 1;000. Note that since thelife style vector contains the probability of each life stylethat sum to 1, each entry of the life style vector is randomlygenerated between 0 and 1, and normalized to guaranteethe sum of the values in the vector is equal to 1. For eachuser, the similarities between itself and all the other userscan be calculated based on the similarity metric in Eq. (4)and the 100 most similar users are chosen as its true friends,denoted as Gi for user i. After the life styles are uploaded tothe system, a friend-matching graph can be constructed andeach user has an impact. We then let each user query thesystem and obtain its friend recommendation results. Let Fi

denote the set of recommended friends. The following mea-surement metrics are used for performance evaluation:

� Recommendation precision Rp. The average of the ratioof the number of recommended friends in the set oftrue friends of the query user over the total numberof recommended friends.

Rp ¼P

i jFi \Gij=jFij1;000

;

where j � j denotes the number of elements in a set�.The dominator is 1,000 because Rp is the average of1,000 users in one experiment.

� Recommendation recall Rr. The average of the ratio ofthe number of recommended friends in the set oftrue friends of the query user over the number of theset of true friends of the query user, which is 100 inour experiments:

Rr ¼P

i jFi \Gij=jGij1;000

¼P

i jFi \Gij=1001;000

:

Using these performance metrics, we study the effect of afew parameters, such as the similarity threshold, Sthr, forfriend-matching graph construction, and the recommenda-tion coefficient, b, for balancing users’ preference on impact

Fig. 10. The gray image representation of the eight users’ similarity.

TABLE 2User Impact Ranking of Eight Users


and similarity. For all the simulation results presented inthis paper, each data point is an average of 100 experiments.

8.2.1 Effect of Similarity Threshold Sthr

We first evaluate the effect of the similarity threshold Sthr

which is important for friend-matching graph construction.When Sthr is too small, almost all users in the system are con-nected, which increases the overhead for graph constructionandmaintenance as well as the ranking process. While if Sthr

is too large, the friend-matching graph cannot reflect the truerelationships between users since those with high similaritymay not be connected with each other. Therefore, the valueof the similarity threshold for the friend-matching graphconstruction should be carefully selected.

Fig. 11 shows the effect of the similarity threshold onthe recommendation recall. We only consider the case that100 friends are recommended. In this case, the recommen-dation precision equals the recommendation recall. We firstobserve that the recommendation recall drops when Sthr

increases. The reason is that links with small similarities aregradually removed from the graph as Sthr increases. Wealso notice that the influence of Sthr is more obvious forsmaller b. This is because removing links affects the impactrankings of users and meanwhile smaller b relies more onthe impact rankings. In our following experiments, wechoose Sthr ¼ 0:5when we evaluate other parameters.

8.2.2 Effect of Recommendation Coefficient b

b is introduced to characterize users’ preference on impactand similarity. Figs. 12a and 12b show the evaluation resultsof b on recommendation recall and precision, respectively.

When b ¼ 0, the recommendation recall is relatively lowwhich is just a little better than random recommendation.This is because the recommendation only relies on theimpact rankings of users rather than similarities betweenusers with the query user. The recommendation recallincreases as b increases because similarity is more and moreemphasized. When b reaches 1, all friends in a query user’sgroup are recommended. Note that even when b is not toolarge (e.g., b ¼ 0:3), the recommendation recall is still highand reaches 100 percent quickly as the number of recom-mended friends increases. As shown in Fig. 12, the recom-mendation precision decreases when the number ofrecommendation friends increases and finally reaches

10 percent when 1;000 users are recommended. This isbecause we only choose 100 users as the set of true friendsfor each user. The recommendation precision also increaseswhen b increases because similarity plays more and moreimportant roles as b increases.

It is obvious that our recommendation is much betterthan random recommendation which is usually 10 percenton average. However, note that we cannot conclude that thelarger b the better, as shown in Fig. 12. Because users mayprefer impact to similarity and our metrics cannot reflectthe recommendation accuracy on impact. To better charac-terize the recommendation results, we define a new metric,called compromise score, as follows:

B ¼X100j¼1

Sði; ijÞrij ;

where B is the compromise score and ij is the jth recom-mended friend for the query user i. In this experiment,100 friends are recommended for each query user. Fig. 13shows the impact of b on the metric. As we mentionedbefore, each data point is an average of 100 experiments.We can see that the metric achieves its maximum when b isaround 0:3. Although the recommended friends have highimpact when b is small, the similarity between them andthe query user is low. In contrast, the similarity between therecommended friends and the query user is high, but theirpopularity is low. Therefore, in order to well balance thesimilarity and popularity, b should be carefully selected.

Fig. 11. Evaluation on similarity threshold. The number of returned rec-ommended friends is 100.

Fig. 12. Evaluation on recommendation coefficient b.


Our system leaves the setting of b to users, so they can findfriends based on their preferences.

8.3 Resource Consumption

Finally, we evaluate the energy consumption performanceof the Friendbook client application. Usually, the user willnot have the incentive to use the application if the batteryruns out in less than 10 hours, which is the typical hour ofusage for a day (the user can re-charge the battery duringthe night). Therefore, energy consumption is another impor-tant metric that has to be measured. We test the energy con-sumption of the same smartphone under two modes: idlemode with Friendbook off and active mode with Friend-book on. Either mode is under a user’s normal use such asmaking phone calls, checking emails, sending SMS, etc. Asshown in Fig. 14, Friendbook drops the battery to 15 percentin about 13 hours. The evaluation shows that Friendbookachieves satisfactory results on the energy performance.

In addition to energy, as shown in Table 3, we monitorthe runtime resources that our client application consumescomparing to other Android system services and applica-tions. It is easily observed that the client application is smallin size while the runtime memory usage is comparable tosome popular applications, such as Google Maps. In addi-tion, the wireless data usage is low so that the user does notneed to worry about the data plan of their mobile phone.Moreover, even when we turn on GPS, accelerometer andgyroscope sensors all the time, the total CPU usage of theclient application is still very low on average.

9 CONCLUSION AND FUTURE WORK

In this paper, we presented the design and implementationof Friendbook, a semantic-based friend recommendationsystem for social networks. Different from the friend recom-mendation mechanisms relying on social graphs in existingsocial networking services, Friendbook extracted life stylesfrom user-centric data collected from sensors on the smart-phone and recommended potential friends to users if theyshare similar life styles. We implemented Friendbook on theAndroid-based smartphones, and evaluated its performanceon both small-scale experiments and large-scale simulations.The results showed that the recommendations accuratelyreflect the preferences of users in choosing friends.

Beyond the current prototype, the future work can befour-fold. First, we would like to evaluate our system onlarge-scale field experiments. Second, we intend to imple-ment the life style extraction using LDA and the iterativematrix-vector multiplication method in user impact rankingincrementally, so that Friendbook would be scalable tolarge-scale systems. Third, the similarity threshold used forthe friend-matching graph is fixed in our current prototypeof Friendbook. It would be interesting to explore the adap-tion of the threshold for each edge and see whether it canbetter represent the similarity relationship on the friend-matching graph. At last, we plan to incorporate more sen-sors on the mobile phones into the system and also utilizethe information from wearable equipments (e.g., Fitbit,iwatch, Google glass, Nike+, and Galaxy Gear) to discovermore interesting and meaningful life styles. For example,we can incorporate the sensor data source from Fitbit, whichextracts the user’s daily fitness infograph, and the user’splace of interests from GPS traces to generate an infographof the user as a “document”. From the infograph, one caneasily visualize a user’s life style which will make moresense on the recommendation. Actually, we expect to incor-porate Friendbook into existing social services (e.g., Face-book, Twitter, LinkedIn) so that Friendbook can utilizemore information for life discovery, which should improvethe recommendation experience in the future.

ACKNOWLEDGMENTS

The authors would like to thank the anonymous reviewerswhose insightful comments have helped improve thepresentation of this paper significantly. This work wassupported in part by NSF CNS-1017156 and CNS-0953238,and in part by NSFC 61273079, ANRNSFC 61061130563,NSFC 61373167 and Natural Science Foundation of HubeiProvince No. 2013CFB297.

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Zhibo Wang received the B.E. degree in Auto-mation from Zhejiang University, China, in 2007,and his Ph.D. degree in Electrical Engineeringand Computer Science from University ofTennessee, Knoxville, in 2014. He is currently anassociate professor with the School of Computer,Wuhan University. His research interests includewireless sensor networks and mobile sensingsystems. He is a student member of the IEEE.

Jilong Liao received the BE degree from theUniversity of Electronic Science and Technology,China, in 2010. He received the master’s of sci-ence degree from the University of Tennessee,Knoxville in 2013. His major research interestsincluded mobile system, data analysis, distrib-uted system, and wireless sensor networks. Heis currently working at Microsoft’s Operating Sys-tem Group (OSG) as a software developmentengineer.

Qing Cao received the BS degree from FudanUniversity, China, the MS degree from the Uni-versity of Virginia, and the PhD degree from theUniversity of Illinois in 2008. He is currently anassistant professor in the Department of Electri-cal Engineering and Computer Science at theUniversity of Tennessee. His research interestsinclude wireless sensor networks, embeddedsystems, and distributed networks. He is a mem-ber of the ACM, IEEE, and the IEEE ComputerSociety.


Hairong Qi received the BS and MS degreesin computer science from Northern JiaoTongUniversity, Beijing, China, in 1992 and 1995,respectively, and the PhD degree in computerengineering from North Carolina State University,Raleigh, in 1999. She is currently a professorwith the Department of Electrical Engineeringand Computer Science at the University ofTennessee, Knoxville. Her current research inter-ests are in advanced imaging and collaborativeprocessing in resource-constrained distributed

environment, hyperspectral image analysis, and bioinformatics. Shereceived the US National Science Foundation (NSF) CAREER Award.She also received the Best Paper Award at the 18th International Confer-ence on Pattern Recognition and the third ACM/IEEE InternationalConference on Distributed Smart Cameras. She recently receives theHighest Impact Paper from the IEEE Geoscience and Remote SensingSociety. She is a senior member of the IEEE.

Zhi Wang received the BS degree fromShenyang Jian Zhu University, China, in 1991,the MS degree from Southeast University, China,in 1997, and the PhD degree from ShenyangInstitute of Automation, the Chinese Academy ofSciences, China, in 2000. During 2000-2002, asa postdoc, he has conducted research in InstitutNational Polytechique de Lorraine, France andZhejiang University, China, respectively. During2010-2011, he has conducted research as anadvanced scholar at UW-Madion. He is currently

an associate professor in the Department of Control Science and Engi-neering of Zhejiang University. His research interests include wirelesssensor networks, visual sensor networks, industrial communication andsystems, and networked control systems. He is a member of the IEEEand ACM.

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Documents

538 IEEE TRANSACTIONS ON MOBILE COMPUTING, · PDF fileFriendbook: A Semantic-Based Friend Recommendation System for Social Networks Zhibo Wang, Student Member, IEEE, Jilong Liao, Qing