1. Social Recommendation
Yuan Quan (袁 泉)
IBM Research - China

2. About me
• Yuan Quan
– M.S. Computer Science and Engineering, Xi’an Jiaotong
University, 2003-2006.
– B.S. Computer Science and Engineering, Xi’an Jiaotong
University, 1999-2003.
• 2006 ~ now IBM China Research Lab
• Research interest
– Personalized recommendation
– User modeling
– Social network analysis

3. Agenda
• Social Recommendation
– Categories & samples
– Definition
• Concept-level Overview
• Effectiveness of Social Relationship
• Technologies on Social Fusion
– Pair-wise similarity fusion
– Graph-based fusion
• Graph-based data models
• Algorithms

4. Social Recommendation Categories
• Collaborative Filtering is a kind of social recommender
– compare with traditional content-based approach
• Recommendation from friends
– Offline: daily recommendation from friends
– Online: news feeds from friends on Facebook, Re-tweet, 开心转帖
• Any recommendation using social data as input
– Social relationship / social network
• friendship, membership, trust/distrust, follow
– Social tagging & bookmarking
• Recommendation over Social Media (Blog, YouTube)

5. Collaborative Filtering - Amazon

6. Friends’ Recommendation – Facebook

7. Social Recommendation based on massive
people’s wisdom

8. Recommending Friends via Social Network

9. Music Recommendation based on Taste &
Friendship/Membership

10. Agenda
• Social Recommendation
– Categories & samples
– Definition
• Concept-level Overview
• Effectiveness of Social Relationship
• Technologies on Social Fusion
– Pair-wise similarity fusion
– Graph-based fusion
• 5 graph-based data models
• Algorithms
– Random walk
– Class label propagation - adsorption

11. Social Recommendation Overview
Input: Output:
Information item
User-Item (Rating) Algorithms
Merchandise/Ads
User/Item KNN; Clustering-based
Social Relations Graph-based Algorithms People
Matrix Factorization
Social Tagging Information Diffusion
Community
Probabilistic Model…
Context:
Time Location Query

12. Effectiveness of Social Relationship
• CF vs SF Familiarity vs Similarity
• Social filtering approach outperforms the • Extensive user survey with 290 participants and a field study
including 90 users, indicates superiority of the familiarity network as
CF approach in all variants of the
a basis for recommendations
experiment • Trustworthy
G. Groh et.al, Recommendations in Taste Related Domains: I.Guy, et.al Personalized Recommendation of Social Software Items
Collaborative Filtering vs. Social Filtering, GROUP07 Based on Social Relations, ACM Recsys09

13. Agenda
• Social Recommendation
– Categories & samples
– Definition
• Concept-level Overview
• Effectiveness of Social Relationship
• Technologies on Social Fusion
– Pair-wise similarity fusion
– Graph-based fusion
• 5 graph-based data models
• Algorithms
– Random walk
– Class label propagation - adsorption

14. Fusing via weighted-similarity
friendship only
Item User
Ia Ib Ic Ua Ub Uc
Ua 1 0 1 Ua 1 0 1
User User
Ub 0 1 0 Ub 0 1 0
Uc 1 1 0 Uc 1 0 1
User-Item Matrix Friendship Matrix
Simui Simfri
Neighborhood Similarity Formula:
Simui+fri(ua,ub) = λ *Simui(ua,ub) + (1-λ)*Simfri (ua,ub)
Optimal λ was learned by cross-validation
Konstas, et, al. On social networks and collaborative recommendation, SIGIR09
Yuan, et, al. Augmenting Collaborative Recommender by Fusing Explicit Social Relationships. ACM
RecSys09, workshop of Social Recommender

15. Fusing via weighted-similarity
membership only
Item Group
Ia Ib Ic Ga Gb Gc
Ua 1 0 1 Ua 0 0 1
User User
Ub 0 1 0 Ub 0 1 1
Uc 1 1 0 Uc 1 0 0
User-Item Matrix Membership Matrix
Simui Simmem
Neighborhood Similarity Formula:
Simui+mem(ua,ub) = λ *Simui(ua,ub) + (1-λ)*Simmem(ua,ub)

16. Fusing via weighted-similarity
friendship + membership
Item User Group
Ia Ib Ic Ua Ub Uc Ga Gb Gc
Ua 1 0 1 Ua 1 0 1 Ua 0 0 1
User User User
Ub 0 1 0 Ub 0 1 0 Ub 0 1 1
Uc 1 1 0 Uc 1 0 1 Uc 1 0 0
User-Item Matrix Friendship Matrix Membership Matrix
Simui Simfri Simmem
Neighborhood
Similarity
Formula: Simui+fri+mem(ua,ub) = λSimui + (1-λ)[β Simmem + (1-
β)Simfri ]
Optimal λand β was learned by cross-validation

17. Experimental results cont.
• The baseline is user-based CF on user-item matrix only by cosine similarity

18. Agenda
• Social Recommendation
– Categories & samples
– Definition
• Concept-level Overview
• Effectiveness of Social Relationship
• Technologies on Social Fusion
– Pair-wise similarity fusion
– Graph-based fusion
• 5 graph-based data models
• Algorithms
– Random walk
– Class label propagation - adsorption

19. Model 1: Classic user-item bipartite graph
with attributes
attributes age gender loc
item i1 i2 i3
user u1 u2 u3
attributes category color price

20. Model 2: user-item bipartite graph with
social relationships
user item Ga Ia
Ua
i1
u1
Gb Ib
Ub
friendship u2 i2
Gc Ic
Uc
u3 i3
U user node
membership
friendship I item node
user’s behavior on item
G group node

21. Model 3: Triple models & Temporal models
tag group
user item user item
User-Item-Tag User-Item-Group

22. Model 4: Temporal Models
• Information flow
– u and r have 40 items in common
– u and v have 40 items in common
Session: a combinational
node of user & item
session
Fig.1 How adoption patterns affect the
recommendations user item
User-Item-Session
Fig.2 illustration of Info. Flow
X. Song et.al, Personalized
Recommendation Driven by Information
Flow, SIGIR 06

23. Model 5
TrustWalker: RW on a trust network
M Jamali, TrustWalker: a random walk model for combining A heterogeneous social network:
trust-based and item-based recommendation, SIGKDD09
User-Resource-Tag-Category
Zhang & Tang, Recommendation over a
Heterogeneous Social Network, WAIM08

24. Agenda
• Social Recommendation
– Categories & samples
– Definition
• Concept-level Overview
• Effectiveness of social relationship
• Technologies on fusing social relationships
– Pair-wise similarity fusion
– Graph-based fusion
• 5 graph-based data models
• Algorithms
– Random walk
– Class label propagation - adsorption

25. Random Walk
• Random walk is a mathematical formalization of a trajectory that consists of taking successive
random steps. Often, random walks are assumed to have Markov properties:
• E.g. the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging
animal, the financial status of a gambler can all be modeled as random walks
One dimension RW
Two dimension RW

26. Random Walk cont.
• RW on graph: PageRank is a random walk on graph
• RW’s usage in recommendation
– For each user, rank & recommend top-N unknown items
– Calculate similarities between nodes
• E.g. user-user nodes similarity for neighborhood
• Similarity measures: Average Commute-Time, Average FPT, L+, etc.
• Notice:
– Transition probability matrix
– Personalized vector
– Damping factor

27. Class propagation - adsorption
Shadow
vertex
1
1
1
Baluja, et.al, Video Suggestion and Discovery for YouTube: Taking Random Walks Through the View Graph, WWW08

28. Our work
• Augmenting Collaborative Recommender by Fusing Explicit Social
Relationships.
– First work to discover membership as useful as friendship in
recommendation.
• ACM RecSys09, workshop of Social Recommender
• Model Users’ Long-/short-term Preference on Graph for
Recommendation.
– First work to balance the influence of long-/short-term preference on
graph
• Submitted to SIGKDD10.
• Temporal Dynamic of Social Trust for Recommendation
– First work to study the temporal dynamics of social relations and its
usage for recommendation
• Draft for ACM Recsys10.

29. Thanks~!