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2.social recommedation

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jilung hsieh
jilung hsieh
TechnologyEntertainment & Humor
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Social Recommendation:The Web Environment We Live By 2013.03.07
Modeling Recommending Feedback Evaluating
Guy & Carmel(2011) in WWW2011 for introducing the section “Social recommender systems”
Guy & Carmel(2011) in WWW2011 for introducing the section “Social recommender systems”
Which store will you pay money?
Modeling users & items
Modeling items - for content-based method
Recommending by items’s content http://Booklamp.org
Problems of content-based recommendations music, comic, movies, ...
Modeling ▸ Modeling users • • Homophily community social networks •
Location-based mobile dating Examples of user modeling http://www.businessinsider.com/best-iphone-dating-apps-2012-6?op=1
Location-based mobile dating http://www.businessinsider.com/best-iphone-dating-apps-2012-6?op=1
Modeling users by their personal features
http://www.bbc.co.uk/news/magazine-21377624
Recommendation
Personal recommendation, modeling users
Modeling users, recommended by social http://www.librarything.com/profile/ElizaJane
Recommendation setting
• • ... • co-bought items
• personalization • community • social network
Social recommendations algorithms
Lady in the Water Snakes on a Plane Just My Luck Superman Returns You, Me and Dupree The Night Listener Lisa Rose 2.5 3.5 3.0 3.5 2.5 3.0 Gene Seymour 3.0 3.5 1.5 5.0 3.5 3.0 Michael Phillips 2.5 3.0 - 3.5 - 4.0 Claudia Puig - 3.5 3.0 4.0 2.5 4.5 Mick LaSalle 3.0 4.0 2.0 3.0 2.0 3.0 Jack Matthews 3.0 4.0 - 5.0 3.5 3.0 Toby - 4.5 - 4.0 1.0 -
Match? Find the most matched users
Simpson distance Normalized Simpson distance
Q: who and who have similar tastes for movies? With closed tastes
Recommending items by matching users
Creating recommendations for Toby Lady in the Water Snakes on a Plane Just My Luck Superman Returns You, Me and Dupree The Night Listener Lisa Rose 2.5 3.5 3.0 3.5 2.5 3.0 Gene Seymour 3.0 3.5 1.5 5.0 3.5 3.0 Michael Phillips 2.5 3.0 - 3.5 - 4.0 Claudia Puig - 3.5 3.0 4.0 2.5 4.5 Mick LaSalle 3.0 4.0 2.0 3.0 2.0 3.0 Jack Matthews 3.0 4.0 - 5.0 3.5 3.0 Toby - 4.5 - 4.0 1.0 -
Find matching products
Finding movies with similar user scores Finding who will be invited to watch the movie
Evaluating Recommendations
N: negative P: positive Rn Rp Precision = Rp Rp+Rn Recall = Rp P
TAM2
Estimated recommending quality along with time decay Volume of data Recommendation quality
• • • • • •
Discussion ▸ Reputation-aware vs. trust-aware systems
Massa, P., & Avesani, P. (2007, October). Trust-aware recommender systems. In Proceedings of the 2007 ACM conference on Recommender systems (pp. 17-24). ACM. “However due to data sparsity of the input ratings matrix, the step of finding similar users often fails.” (Massa & Avesani, 2007) + trust metrics “PageRank, for example, is a global trust metric.” (Massa & Avesani, 2007)
- Top50 Reviews - 374 - 82,711/2,751,778 Reviews Reviews Reviews user DNA
how much should the “source” trust the “sink”? Trust-aware recommendation
Friends or interested community FB http://www.librarything.com/profile/ElizaJane
http://www.anobii.com/shelf_similar
5/8 2/16 16/190 4/87 8/88 2/60
Social tagging
- binary relation
- - tripartite relation
Different users may have common resources Different resources may have common tags Users may have similar tags on their common resources Community Relationship Consensus
What tag can bring to users? • For observing and modeling users • Users’ mood and opinion on objects • Labeling resources by consensus of users

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