“Make New Friends ,but Keep the Old”-Recommending People on

Social Networking Sites

Jilin Chen ,Werner Geyer ,Casey Dugan ,Michael Muller ,Ido Guy

CHI 2009

Outline

• Introduction• Data Set• Algorithm • Experiment– Personalized survey – Controlled field study

• Discussion & Conclusion

Introduction

• Users in online social network site has two type of friends – Already known offline– New friends they discover on the site

• There are many personalized-recommended algorithms , but the effective of those approach is not available

• It is different from traditional recommendations of books, movie, restaurants, etc.

Introduction

• Goal– Effectiveness of different algorithms– The characteristics of recommending known

versus unknown people– If the recommender system effectively increase

the number of friends a user has– Overall impact of a recommender system on the

site

Data Set

• online social network site : Beehive within IBM• Start time: July 2008• Network situation in experiment: 38000 users,

average of 8.2 friends per user.• Friend type: Non-reciprocal friendship

Data Set(Beehive)

Algorithms

• People recommendation algorithms– Content matching• Explanation: common keywords

– Content-plus-link(CplusL)• Explanation: common keywords & directional links

– Friend-of-Friend(FoF)• Explanation: common friend list

– SONAR • Explanation: all relation in database of IBM

Algorithm-Content matching

• Motivation : If we both post content on similar topics, we might be interested in getting to know each other.

• Formulation(similarity of two users) :

• Relationship explanation : show up 10 highest scores words.

Algorithm-Content plus link

• Motivation: By disclosing a network path to a weak tie or unknown person, recipient may be more likely to accept it.

• Link rule(3 and 4 path):

• Similarity scores: if valid link exits ,boost 50% • Relationship explanation : show up 10 highest

scores plus valid links if it exits.

Algorithm-Friend of friend

• Motivation : If many of my friends consider Alice a friend, perhaps Alice could be my friend too.

• Formulation:

• Score : Number of Mutual friends.• Relationship explanation : show up all mutual

friends.

Algorithm-SONAR

• SONAR system : Aggregates social relationship information from public data sources within IBM– Organization chart – Publication database– Patent database– Friending system – People tagging system– Project wiki– Blogging system

Experiment :Personalized survey

• Methodology:– 500 active users – Every user was exposed to all four algorithms

• Top 10 recommendations of four algorithms

Experiment :Personalized survey

• For each recommendation , we show a photo, the job title and the work location ,as well as the explanation generated by a algorithm.

• User answer following Question for the test.

Experiment :Personalized survey

• User also answer more general questions like their interest in meeting people on the site.

• 415 logged in and 230 valid survey form.• Results-Understand user’s need– 95% of the user considered people

recommendations to be useful and would like to see them as a feature on the site.

– 61.6% said they are interested in meeting new people , 31% said maybe and 7.4% say no.

Experiment :Personalized survey

– What may make people to connect to unknown person : 75.2% chose common friends , 74.4% said common content, 39.2% indicated geographical location of the person, 27% said the division within IBM, and 14.5% chose “other”.

Experiment :Personalized survey

Experiment :Personalized survey

Experiment :Controlled field study

• Methodology:– 3000 users– Divide into 5 groups, each with 600 users.4

experiment with one algorithm, 1 control group that did not get any recommendations.

– In experiment group ,show one recommendation a time, starting from the highest ranked ones.

– In control group, we advertised various friending features and actions.

Experiment :Controlled field study

Experiment :Controlled field study

• Valid users: 122 from content matching group, 131 from the content-plus-link group , 157 from the friend-of-friend group, and 210 from the SONAR group.

• Test situation:

Experiment :Controlled field study

• In contrast to survey, the introduction response is less than 1%– “what is this” let the users feel bothered and

ignore the feature• Impact of people recommendations– In experiment group viewed 13.7% more page

compared to previous time– In control group viewed 24.4% less page

compared to previous time

Experiment :Controlled field study

Discussion and conclusion

• The result can show the four algorithm are effective in making people recommendation and increase the number of friends.

• Relationship-based algorithms are better at finding known one ,whereas content similarity algorithms are better at new friends

• To combine the strengths of both type of algorithms, we can initially use R-B algo ,complement them with C-S algo latter.