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Social Recommender Systems

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social relationships provides a new opportunity for improving the quality of recommender systems

TechnologyEntertainment & Humor
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Social Recommender System By: Ibrahim Sana 15.08.08
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Collaborative Filtering (CF) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
User-User Collaborative Filtering ? 3 Active user Rating prediction
CF Limitation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Solution: using trust relationships ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Trust inference ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],a b d c 1 5 3 2 3
Local trust metrics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],a b d c 1 5 3 2 ?
Related works(1):Massa et al(2006) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendation method ,[object Object],Estimated trust userXuser Predicted Ratings MXN Rating predictor Rating MXN Input output
Evaluation and results
Related works(2):Golbeck et al(2006) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Recommendation method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation and results ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Result
Limitations ,[object Object],[object Object],[object Object]
Dominants Social Measures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Motivation ,[object Object],[object Object],[object Object],[object Object]
Research questions ,[object Object],[object Object]
Objectives ,[object Object],[object Object],[object Object],[object Object]
Hypothesis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object],P S
Prediction method 2 ,[object Object],[object Object],[object Object],[object Object],P S
Social-based Prediction ,[object Object]
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 ,[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Social Recommender Systems
Social Recommender Systems

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Social Recommender Systems

  • 1. Social Recommender System By: Ibrahim Sana 15.08.08
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  • 5. User-User Collaborative Filtering ? 3 Active user Rating prediction
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  • 23. 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
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  • 26. Experiment Environment User Authentication Task1: Movies rating Task2: User's social relationships
  • 27. 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
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  • 33. Hybrid method: Cold start users
  • 34. 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
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  • 38.