Role-Based Contextual Recommendation

Cheng Zeng, Jian Wang, Liang Hong(Wuhan University)Jilei Tian, Xiaogang Yang(Nokia)

Nokia and Whu

Background

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Background

Cannot compete the amount of applications/services,Serving on demand is more important

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Background

Search Engine solves the problem of information explosion.Facing the vast and various applications/services (Traditional Software, Widget, REST/SOAP-WS, RSS…), what can we do?

The popularization of smart phones brings opportunities for exploiting personalized recommendation based on rich context information and mobile social networks.

What you think is what you get

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Background

On bus Media Player

Walking RadioMeeting Switch to Vibration E-book

Cooking Shopping AD

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Framework

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Role and Trust

Collaborative filtering (CF) is widely used in recommendation

systems. However, the weakness of CF involves in cold-start,

sparseness of useful information, and internal attacks. Trust-based

recommendation is proposed to improve the question.

Current trust -based recommendation approaches only consider

user’s trust statements and the similarity of user’s rating history,

which oversimplify the trust relationship.

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Role and Trust

“Role” has been mentioned in data security and software

engineering domain. Compared with these work, we adopt roles in

intelligent recommendation system with different connotation.

The advantage of using role: A user generally trusts one/several aspects(roles) of another user’s

interests but not all, namely conditional trust; Role takes common knowledge among different users into account and is

a high-level abstract of a group of mobile users with certain similarity, Interest reasoning among roles is more efficient than among individuals;

Role can distinguish the statements changing for a user based on context information and provide more accurately recommending.

Transfer learning (Cross-domain)

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Advanced User Profile

AUP is built in RDF, which describes user's static and dynamic information: <time, scene, behavior>

AUP supports shopping, music domain currently and will extend for AD and other domains.

User's habit, preference and behavior were learnt by data mining approach with the logged data collected from personal mobile devices

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Advanced User Profile

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Role Mining

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Role MiningPotential role mining algorithm

c1 c2 c3 c4 c5

u1 b1 b1 b1 0 0

u2 0 0 0 b2 b2

u3 b1 b1 b1 b2 b2

u4 b3 b3 0 0 0

u5 b3 b3 0 b2 b2(1) (2)

user

context

(1) (user, context, behavior) table. (2) Mine roles by clustering users’ behavior.(3) Get three potential roles. (4) Get role-context-behavior table

behavior

(3)

(4)

role

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u1 b1 b1 b1 0 0

u2 0 0 0 b2 b2

u3 b1 b1 b1 b2 b2

u4 b3 b3 0 0 0

u5 b3 b3 0 b2 b2

r1 r2 r3

u1 1 0 0

u2 0 1 0

u3 1 1 0

u4 0 0 1

u5 0 1 1

c1 c2 c3 c4 c5

r1 b1

b1

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0 0

r2 0 0 0 b2

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b3

0 0 0

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Role Mining

We create a potential role tree based on FCA(Formal Concept Analysis) and map between the potential role tree and the manually created role ontology. 45%

68%

81%

72%

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Role Mining Experiment

Data Set

Random data

Questionnaire (230)

Flickr

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Role Mining Experiment

Performance test results

#Context=20, #Behavior=20 #User=200, #Behavior=16 #User=100, #Context=10

M1: our methodM2: traditional FCA method

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Trust Mining

We have three ways to calculate trust between users for service

recommending:

Only trust preference fragments of those users when they play same

role with myself

Trust those users who have similar role set

Synthesize two ways above

We consider role relations, user’s dependency to role, and the

weight of each role.

Build trust network among users to provide trust propagation.

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Trust Mining

User1 …… .Usern

User1

……

Usern

Problem:How to decrease calculating times?

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Trust Mining

A B D E

A B C E F

User W

User X

{A8, B7, C5, D4, E3, F3}Role Set: Calculate weight of each role With IDF and rank

( ),

( )

ii j

j

W aa w x a w x

W a

8 7 30.6

8 7 5 4 3 3

0.63 0.32 0.14

0.69 0.42 0.23 ……..

W(0.63) X(0.69) … …. ….

W(0.32) X(0.42) Y(0.68) …. ….

… … ..

ABC

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Trust Mining Experiment

Data Set:

Epinion (have trust relationship information)

Verify the trust is higher between those users with similar role set

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Trust Mining Experiment

Performance test:

Similarity between friends : 0.37 -> 0.51

No role with role

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Trust Mining Experiment

MAE&RMSE test comparing with traditional approach:

Thank You !