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 Recommendation algorithm User profile

3. Common Recommendation Techniques Collaborative Filtering Give recommendations to a user based on preferences of “similar” users Give recommendations to a user based on preferences of “similar” users Preferences on items may be explicit or implicit 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 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 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) 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 P u contains preference scores by u on some other items, {i 1, …, i k } different from i t preference scores on i 1, …, i k may have been obtained explicitly (e.g., movie ratings) or implicitly (e.g., time spent on a product page or a news article) preference scores on i 1, …, i k 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 profile P u for a user u, and a target item i t, predict the preference score of user u on item i t

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 P u 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 an art historian? User is a pop music fan? User is a pop music fan?

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

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 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

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 people add free-text tags to their content where people happen to use the same terms then their content is linked 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. frequently used terms floating to the top to create a kind of positive feedback loop for popular tags.Examples: Del.icio.us Del.icio.us Del.icio.us Flickr Flickr Flickr Last.fm Last.fm Last.fm

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

16. 16 Tagging and Music Recommendation

17. 17 Social / Collaborative Tags

18. 18 Social / Collaborative Tags

19. 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. 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. 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. 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 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. 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 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) 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 Tagging creates community through the overlap of perspectives This leads to the creation of social networks which may further develop and evolve This leads to the creation of social networks which may further develop and evolvesocial networkssocial networks But, does this lead to dynamic evolution of complex concepts or knowledge? Collective intelligence?