Recommender Systems; Social Information Filtering

1. Recommender Systems;Social Information Filtering

2. Web Personalization & Recommender Systems • Dynamically serve customized content (pages, products, recommendations, etc.) to users based on their profiles, preferences, or expected interests • Most common type of personalization: Recommender systems User profile Recommendationalgorithm

3. Common Recommendation Techniques • Collaborative Filtering • Give recommendations to a user based on preferences of “similar” users • Preferences on items may be explicit or implicit • Content-Based Filtering • Give recommendations to a user based on items with “similar” content in the user’s profile • Rule-Based (Knowledge-Based) Filtering • Provide recommendations to users based on predefined (or learned) rules • age(x, 25-35) and income(x, 70-100K) and childred(x, >=3)  recommend(x, Minivan)

4. The Recommendation Task • Basic formulation as a prediction problem • Typically, the profile Pu contains preference scores by u on some other items, {i1, …, ik} different from it • preference scores on i1, …, ik may have been obtained explicitly (e.g., movie ratings) or implicitly (e.g., time spent on a product page or a news article) Given a profilePu for a user u, and a target itemit, predict the preference score of user u on item it

5. Content-Based Recommenders • Predictions for unseen (target) items are computed based on their similarity (in terms of content) to items in the user profile. • E.g., user profile Pu contains recommend highly: and recommend “mildly”:

6. Content-Based Recommender Systems

7. Content-Based Recommenders: Personalized Search Agents • How can the search engine determine the “user’s context”? ? Query: “Madonna and Child” ? • Need to “learn” the user profile: • User is an art historian? • User is a pop music fan?

8. Content-Based Recommenders • Music recommendations • Play list generation Example: Pandora

9. Collaborative Recommender Systems • Collaborative filtering recommenders • Predictions for unseen (target) items are computed based the other users’ with similar interest scores on items in user u’s profile • i.e. users with similar tastes (aka “nearest neighbors”) • requires computing correlations between user u and other users according to interest scores or ratings • k-nearest-neighbor (knn) strategy Can we predict Karen’s rating on the unseen item Independence Day?

10. Collaborative Recommender Systems

11. Collaborative Recommender Systems

12. Collaborative Recommender Systems

13. Movielens Recommender System http://movielens.umn.edu

14. Other Forms of Collaborative and Social Filtering • Social Tagging (Folksonomy) • people add free-text tags to their content • where people happen to use the same terms then their content is linked • frequently used terms floating to the top to create a kind of positive feedback loop for popular tags. • Examples: • Del.icio.us • Flickr • Last.fm

15. Other Forms of Collaborative Filtering • Social Tagging (Folksonomy) • people add free-text tags to their content • where people happen to use the same terms then their content is linked • frequently used terms floating to the top to create a kind of positive feedback loop for popular tags.

16. Tagging and Music Recommendation

17. Social / Collaborative Tags

18. Social / Collaborative Tags

19. Social / Collaborative Tags

20. Social Tagging • By allowing loose coordination, tagging systems allow social exchange of conceptual information. • Facilitates a similar but richer information exchange than collaborative filtering. • I comment that a movie is "romantic", or "a good holiday movie". Everyone who overhears me has access to this metadata about the movie. • The social exchange goes beyond collaborative filtering - facilitating transfer of more abstract, conceptual information about the movie. • Note: the preference information is transferred implicitly - we are more likely to tag items we like than don't like • No algorithm mediating our connection between individuals: when we navigate by tags, we are directly connecting with others.

21. Social Tagging • Deviating from standard mental models • No browsing of topical, categorized navigation or searching for an explicit term or phrase • Instead is use language I use to define my world (tagging) • Sharing my language and contexts will create community • Tagging creates community through the overlap of perspectives • This leads to the creation of social networkswhich may further develop and evolve • But, does this lead to dynamic evolution of complex concepts or knowledge? Collective intelligence?