The document discusses social recommender systems and various approaches used in them. It covers fundamental recommendation techniques like collaborative filtering, content-based recommendation, and knowledge-based recommendation. It also discusses using tags, social relationships, and temporal data in recommendations. Evaluation of recommender systems and challenges are also summarized.

1. Social Recommender Systems Ido Guy, David Carmel IBM Research-Haifa, Israel WWW 2011, March 28 th -April 1 st , Hyderabad, India

3. Introduction IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India

10. Real-World Examples

11. Real-World Examples

12. Real-World Examples

13. Real-World Examples

14. Real-World Examples

16. Fundamental Recommendation Approaches IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India

18. Recommendation techniques ( Burke,2002 ) Generate a classifier based on u ’s ratings, use it to classify new items Rating from u to items Features of items Content-based Infer a match between items and u ’s needs User needs Features of items Knowledge-based Identify similar users, extrapolate from their rating Demographic information about u Demographic information about users Demographic Identify similar users, extrapolate from their rating Rating from u to items User-item matrix CF Process Input Background Technique

22. Cosine based similarity between users Pearson based similarity between users

29. Content Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

45. Tag Recommendation IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India

49. Flickr’s tag recommender (Sigurbjornsson WWW2008 )

54. People Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

67. Community Recommendations IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

76. Recommendations for Groups IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India

78. Issues in recommendation for groups How to support the process of arriving at a final decision Negotiation may be required. Members decide which recommendation (if any) to accept Aggregation methods Aggregating preferences/results must be applied System generates recommendations Benefits/drawback for the group/system Members can examine each other Members specify their preferences General issues Differences from individuals Phase of recommendation

79. Explicit specification of preferences: MusicFX ( McCarthy, CSCW 2000) Hate Don’t mind Love Alternative rock : 1 2 3 4 5 Hot country: 1 2 3 4 5 50’oldies 1 2 3 4 5 …90 more genres

80. Collaborative speciation of preferences in the Travel Decision Forum [Jameson 04]

83. Aggregation Methods [Berkovsky, RecSys ’10]

86. The Cold Start Problem IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

95. Trust and Distrust IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India

99. Social trust graph User-item rating matrix Problem Definition

100. TidalTrust: Accumulate recommendation from trusted people only (Golbeck06)

102. Inferring Trust The Goal: Select two individuals - the source and sink - and recommend to the source how much to trust the sink . Sink Source

106. Trust in Recommendation (by explanations) Good explanations could help inspire user trust and loyalty, increase satisfaction, make it quicker and easier for users to find what they want, and persuade them to try or purchase a recommended item MoviExplain: A Recommender System with Explanations (Symeonidis09)

110. Social-based Explanation (Guy09)

112. Social Recommender Systems in the Enterprise IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

114. IBM Lotus Connections – Enterprise Social Software

121. Temporal Aspects in Social Recommendation IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

127. Time-based Evaluation in SRS Google Reader News Recommendation Network effects of people recommendation Increasing engagement of new users

130. Social Recommendation over Activity Streams IBM Research WWW 2011, March 28 th -April 1 st , Hyderabad, India

141. Evaluation Methods IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India

150. Questions to measure subjective constructs

154. Open Issues and Research Challenges IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India