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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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

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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)

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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.