social recommender systems
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Social Recommender System
By: Ibrahim Sana
15.08.08
Agenda
Introduction Background on Collaborative Filtering Collaborative Filtering Limitation Using trust in RS Related works Research methodology Evaluation and Results Conclusion
Introduction
Recommender system (RS) help users find items (e.g., news items, movies) that meet their specific needs.
Motivation Information overload
Researches in RS focused on developing methods and approaches dealing with the Information overload problem.
Main Approaches Content-Based (Salton, 1989) Collaborative filtering/Social Filtering (Goldberg, 1992 ) hybrid
Collaborative Filtering (CF)
In the real world we seek advices from our trusted people
CF automate the process of “word-of-mouth” General use:
Weight all users with respect to similarity with the active user.
Select a subset of the users (neighbors) to use as predictors (recommenders).
Rating prediction:
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Active userActive user
Rating prediction
User-User Collaborative Filtering
CF Limitation
New item problem Cold start problem Sparsity (95%-99%) Controversial user Easy to attacks Scalability Cannot recommend items to someone with
unique tastes. Tends to recommend popular items
Solution: using trust relationships
Implicit: Deriving trust score directly from the rating data Generally based on user prediction accuracy in the past
Explicit: users explicitly “rate” other users FilmTrust (Hendler et al,2006) Molskiing (Massa et al,2005)
Limitation: Users have on average very few links (trusted sources) More User’s effort
Solution Trust propagation: find unknown user’s
trustworthiness based on the users’ “web of trust”
Trust inference
Global metrics: computes a single global trust value for every single user (reputation)
Examples: PageRank (Page et al, 1998),eBuy
Pros: Based on the whole community opinion Simple to compute
Cons: Trust is subjective (controversial users)
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Local trust metrics Local metrics: predicts (different) trust scores that are
personalized from the point of view of every single user
Example: MoleTrust (Massa et al,2006) TidalTrust (Golbeck et al,2005)
Pros: More accurate Attack resistance
Cons: Ignoring the “wisdom of the crowd” More complicated
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Related works(1):Massa et al(2006)
Crawling Epinion.com users can review items and also assign them numeric
ratings in the range 1 to 5. Users can also express their “Web of Trust” and their
Black list Dataset:
~50K users,~140K items,~665K reviews 487K binary trust statement Sparsity=99.99135%
Above 50% are cold start users (less than 5 review)
Recommendation method
Using MoleTrust metric
Estimated trust userXuser
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Evaluation and results
Related works(2):Golbeck et al(2006)
FilmTrust: Online Recommender System Users can rate films, write reviews, and express trust
statements in other users based on how much they trust their friends about movies ratings
Rating scale from half start to four start Trust scale from 1 to 10 Dataset:
500 users, 100 popular movies, 11,250 rating 350 users with social connection Sparsity=77%
Recommendation method
Weight ratings by trust value Search recursively for trusted sources Using TidalTrust metric for trust inference Simple Prediction method
Example:Alice trust Bob 9Alice trust Chuck 3Bob rates the movie “Jaws” with 4 starsChuck rates the movie “Jaws” with 2 stars
Alice’s predicted rating for “Jaws” is: (9*4+3*2)/9+3=3.5
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Evaluation and results
Benchmarks: Pure CF and simple average 80% training and 20% testing Using MAE metric First analysis, using trust didn’t appear to be effective
Above 50% of the rating were within the range of the mean +/- half star
Trust-based significantly useful only to user who disagree with the average
Result
Limitations
Do not distinguish between various types of social relationships
Researches in marketing and in applied psychology identified different types of social measures impact recipient’s advice taking
Different types of social relations impact recipient’s advice taking in different ways
Dominants Social Measures
Cognitive similarity (Gilly et al. 1998) Tie-Strength (Levin & Cross 2004)
Relationship duration Interaction frequency Closeness
Trust (Smith et al. 2005) Competence Benevolence Integrity
Social Capital/Reputation (Gilly et al. 1998)
Motivation
Web 2.0 provide opportunity for peoples to interact with each other Social networks (trust, friendships) Electronic communications (Tie-Strength) Reputation mechanisms (Social Capital)
Research questions
Can additional relationship information be utilized to enhance recommender system performance?
What types of social relation is most useful?
Objectives
Identify the difference between similarity based CF and social based CF
Explore the contribution of various social relations
Suggest solution for the cold start problem
Suggest solution for the scalability problem
Hypothesis
H1:Null Hypothesis: social relationships don’t provide any contribution to the performance of recommender systemsAlternative Hypothesis: social relationships do contribute to the performance of recommender systems
H2:Null Hypothesis: different social relationships provide different contribution to the performance of recommender systems.Alternative Hypothesis: different social relationships provide similar contribute to the performance of recommender systems
H3:Null Hypothesis: different social relationships provide different contribution to the performance of recommender systems.Alternative Hypothesis: different social relationships provide similar contribute to the performance of recommender systems
Social dimensions and measurement
Social dimension Measurement
Trust I trust this person
Friendship I would consider this person a friend
Interaction Frequency How often did you communicate with this person
Relationship Duration How long have you known this person
Social capital This person is reputable
Research Method Domain: movie recommendation Subject : 97 4th years student from the IS department
(with social relationships) Tasks:
Provide rating for 160 (popular) items (5 point scale) Select three subject and indicate your social
relationships Some of the relationships we examined
Trust Friendship Interaction duration Interaction frequency Reputation
Research method
Baseline:User-based CF(Pearson Correlation)
Hybrids method (Similarity and Social relations)
(Combination schemes)
Independent Variables (recommendation methods)
Social Restriction method(Pearson Correlation)
Control Variable
Students with social relationships
Tasks
1-Movies rating
2-Social network building
Subjects
Dependent Variables (Performance)
MAE
Precision and Recall
Coverage
Research Method
Experiment Environment
User Authentication
Task1: Movies rating
Task2: User's social relationships
Research framework
Recipient-Source similarity
Past Ratings
Recipient Sources
Systems Prediction
Component
System’s Prediction (Recommendation)
System’s Receiver-Source Similarity
Calculation
System’s Source Qualification Component
(Recipient’s) Sources’
Qualifications
Reputation
Trust, Friendship
Interaction duration, frequency
Prediction method 1
Hybrid method Social relations combined with similarity (Pearson
Correlation) Tuning the source’s weight according to his group Group P: sources similar to the active user Group S: sources belong to the social network of
the active user
Otherwise
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Prediction method 2
Social restriction Social relations used for restriction Consider only sources belong to both groups
S and P Using the source’s similarity
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Simulation System Architecture
Fold generationRandomly generate 10 folds (20% testing, 80% training)
User-Item Rating
Users’ Folds
Users’ Similarity
Users’ Social Network
User-User Similarity generation
Social Network Propagation(distance 1 to 6)
Similarity-Based CF(Pearson Correlation)
Hybrid CF(Pearson Correlation
and Social ties)
Social restriction CF
Configuration UtilityFront-end
Offline-Online boundary
Results (Hybrid method)
Social weighting
0.68
0.7
0.72
0.74
0.76
0.78
0.8
0.82
pure CFd=1d=2d=3d=4d=5d=6
Prediction method
Precision
Recall
Social weighting coverage
70
72
74
76
78
80
82
84
86
pure CFd=1d=2d=3d=4d=5d=6
Prediction method
Co
vera
ge
coverage
Social weighting
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
pure CFd=1d=2d=3d=4d=5d=6
Prediction method
MA
E
MAE
Social Weight Impact
0.74
0.7405
0.741
0.7415
0.742
0.7425
0.743
0.7435
0.744
0.7445
0102030405060708090100
Social tie weight
MA
E
MAE
Hybrid method: Cold start users
Impact of shared-interest sources
0
0.2
0.4
0.6
0.8
1
1.2
1.4
135791113151719212325
Number of sources
MAE
MAE-CF
MAE-WAA1
Impact of different social measures
Social measures AMAE Precision Recall Improvements
Cognitive similarity 0.822165 0.798522 0.731773
Tie-Strength 0.746838 0.795328 0.749967 9.162064
relationship duration 0.742796 0.796085 0.746972 9.65368
interaction frequency 0.748337 0.795426 0.750318 8.979698
Closeness 0.74938 0.794472 0.752611 8.852814
Trust 0.738412 0.796603 0.743428 10.18684322
competence 0.738428 0.797798 0.741061 10.1849155
benevolence 0.736044 0.795776 0.744768 10.474944
Integrity 0.741934 0.795456 0.746152 9.7585475
Social capital 0.744079 0.797383 0.74192 9.497685
Result (Social restriction)
Social restriction
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
pure CFd=1d=2d=3d=4d=5d=6
Prediction method
MA
E
MAE
Social restriction
0.66
0.68
0.7
0.72
0.74
0.76
0.78
0.8
0.82
pure CFd=1d=2d=3d=4d=5d=6
Prediction method
Precision
Recall
Social restrictin coverage
0
10
20
30
40
50
60
70
80
pure CFd=1d=2d=3d=4d=5d=6
Prediction method
Co
vera
ge
coverage
Social restriction: cold start users
Shared interest sources impact
0
0.5
1
1.5
2
2.5
135791113151719212325
Number of sources
MAE
MAE-CF
MAE-WAA1
Conclusion
Social relationships is effective in alleviating CF weaknesses: Cold start problem (Social weighting and
social restriction) Scalability problem (Social restriction) Spammers attacks (Social weighting and
social restriction)
References Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ’Word of
Mouth’. In: Proceedings of Human Factors in Computing Systems, pp.10–217 (1995)
Herlocker, J., Konstan, J.A., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22, 5–53(2004)
Massa, P., Avesani, P.: Trust-Aware Collaborative Filtering for Recommender Systems.In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS), Agia Napa, Cyprus, pp. 492–508 (2004)1060 C.-S. Hwang and Y.-P. Chen
Avesani, P., Massa, P., Tiella, R.: Moleskiing: A Trust-Aware Decentralized Recommender System. In: Proceedings of the First Workshop on Friend of a FriendSocial Networking and the Semantic Web, Galway, Ireland (2004)
Golbeck, J: Generating Predictive Movie Recommendations from Trust in Social Networks. Proceedings of the Fourth International Conference on Trust Management. Pisa, Italy, May 2006.
R. Guha, R. Kumar, P.:Raghavan, and A. Tomkins. Propagation of trust and distrust. In Proc. of the Thirteenth International World Wide Web Conference, MAY 2004.
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