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

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

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:

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:

User-User Collaborative Filtering ? 3 Active user Rating prediction

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

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”

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) a b d c 1 5 3 2 3

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)

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 a b d c 1 5 3 2 ?

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

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)

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 Predicted Ratings MXN Rating predictor Rating MXN Input output

Using MoleTrust metric

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%

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 9 Alice trust Chuck 3 Bob rates the movie “Jaws” with 4 stars Chuck rates the movie “Jaws” with 2 stars Alice’s predicted rating for “Jaws” is: (9*4+3*2)/9+3=3.5

Weight ratings by trust value

Search recursively for trusted sources

Using TidalTrust metric for trust inference

Simple Prediction method

Example:

Alice trust Bob 9

Alice trust Chuck 3

Bob rates the movie “Jaws” with 4 stars

Chuck rates the movie “Jaws” with 2 stars

Alice’s predicted rating for “Jaws” is: (9*4+3*2)/9+3=3.5

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

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

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)

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)

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?

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

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 systems Alternative 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

H1 :Null Hypothesis: social relationships don’t provide any contribution to the performance of recommender systems

Alternative 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 Measurement Social dimension This person is reputable Social capital How long have you known this person Relationship Duration How often did you communicate with this person Interaction Frequency I would consider this person a friend Friendship I trust this person Trust

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

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

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 P S

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

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 P S

Social restriction

Social relations used for restriction

Consider only sources belong to both groups S and P

Using the source’s similarity

Social-based Prediction Prediction item i to user u

Prediction item i to user u

Simulation System Architecture

Results (Hybrid method)

Hybrid method: Cold start users

Impact of different social measures 9.497685 0.74192 0.797383 0.744079 Social capital 9.7585475 0.746152 0.795456 0.741934 Integrity 10.474944 0.744768 0.795776 0.736044 benevolence 10.1849155 0.741061 0.797798 0.738428 competence 10.18684322 0.743428 0.796603 0.738412 Trust 8.852814 0.752611 0.794472 0.74938 Closeness 8.979698 0.750318 0.795426 0.748337 interaction frequency 9.65368 0.746972 0.796085 0.742796 relationship duration 9.162064 0.749967 0.795328 0.746838 Tie-Strength 0.731773 0.798522 0.822165 Cognitive similarity Improvements Recall Precision AMAE Social measures

Result (Social restriction)

Social restriction: cold start users

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)

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.

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.