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.
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
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
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
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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)
154. Open Issues and Research Challenges IBM Research WWW 2011, March 28th-April 1st, Hyderabad, India
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Notes de l'éditeur
The sites in the top sites lists are ordered by their 1 month alexa traffic rank. The 1 month rank is calculated using a combination of average daily visitors and pageviews over the past month. The site with the highest combination of visitors and pageviews is ranked #1.
Facebook – updated March 2011 Twitter – June 2010 YouTube – May 2010
To mention that table entries can be filled by implicit feedback
Outside the box – since similar users can recommend surprising items compared to content-based approaches which is limited to items that fit the user profile
What a Difference a Group Makes:Web-Based Recommendations for Interrelated Users (Mason & Smith)
Explaining collaborative filtering
Tagsplanations: explaining recommendations using tags, Vig, IUI09
User Evaluation Framework of Recommender Systems, Chen (SRS 2010)
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