Recommender systemms search engines

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  • 1. RECOMMENDER SYSTEMS ANDSEARCH ENGINES – TWO SIDES OFTHE SAME COIN!?Bracha ShapiraLior RokachDepartment of Information Systems EngineeringBen-Gurion University
  • 2. CONTENT Introduction Applications Methods Recommender Systems vs. search engines
  • 3. ARE YOU BEING SERVED? What are you looking for? Demographic – Age, Gender, etc. Context-  Casual/Event  Season  Gift Purchase History  Loyal Customer  What is the customer currently wearing?  Style  Color Social  Friends and Family  Companion
  • 4. RECOMMENDER SYSTEMS A recommender system (RS) helps people that have not sufficient personal experience or competence to evaluate the, potentially overwhelming, number of alternatives offered by a Web site.  In their simplest form RSs recommend to their users personalized and ranked lists of items  Provide consumers with information to help them decide which items to purchase
  • 5. EXAMPLE APPLICATIONS
  • 6. WHAT BOOK SHOULD I BUY?
  • 7. WHAT MOVIE SHOULD I WATCH? • The Internet Movie Database (IMDb) provides information about actors, films, television shows, television stars, video games and production crew personnel. • Owned by Amazon.com since 1998 • 796,328 titles and 2,127,371 people • More than 50M users per month.
  • 8. abcd The Nextflix prize story In October 2006, Netflix announced it would give a $1 million to whoever created a movie- recommending algorithm 10% better than its own. Within two weeks, the DVD rental company had received 169 submissions, including three that were slightly superior to Cinematch, Netflixs recommendation software After a month, more than a thousand programs had been entered, and the top scorers were almost halfway to the goal But what started out looking simple suddenly got hard. The rate of improvement began to slow. The same three or four teams clogged the top of the leader-board. Progress was almost imperceptible, and people began to say a 10 percent improvement might not be possible. Three years later, on 21st of September 2009, Netflix announced the winner. 13.10.2012
  • 9. WHAT NEWS SHOULD I READ?
  • 10. WHERE SHOULD I SPEND MY VACATION? Tripadvisor.com I would like to escape from this ugly an tedious work life and relax for two weeks in a sunny place. I am fed up with these crowded and noisy places … just the sand and the sea … and some “adventure”. I would like to bring my wife and my children on a holiday … it should not be to expensive. I prefer mountainous places… not too far from home. Children parks, easy paths and good cuisine are a must. I want to experience the contact with a completely different culture. I would like to be fascinated by the people and learn to look at my life in a totally different way.
  • 11. Usage in the market/products RecommendationState-of-the-art solutions Examined Solutions Method Commonness Jinni Taste Kid Nanocrowd Clerkdogs Criticker IMDb Flixster Movielens Netflix Shazam Pandora LastFM YooChoose Think Analytics Itunes AmazonCollaborative Filtering v v v v v v v v v v v vContent-Based Techniques v v v v v v v v v v vKnowledge-Based Techniques v v v v v v vStereotype-Based Recommender Systems v v v v v v vOntologies and Semantic Web Technologies for v v vRecommender SystemsHybrid Techniques v v v v v v vEnsemble Techniques for Improving v futureRecommendationContext Dependent Recommender Systems v v v v v vConversational/Critiquing Recommender v vSystemsCommunity Based Recommender Systems and v v v v vRecommender Systems 2.0 13.10.2012
  • 12. COLLABORATIVE FILTERING
  • 13. Collaborative FilteringDescription  The method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of CF approach is that 1 Collaborative Filtering those who agreed in the past tend to agree again in the future. Selected Techniques kNN - Nearest Neighbor  SVD – Matrix Factorization  Similarity Weights Optimization (SWO)  13.10.2012
  • 14. COLLABORATIVE FILTERING abcdThe Idea Trying to predict the opinion the user will have on the different items and be able to recommend the “best” items to each user based on: the user’s previous likings and the opinions of other like minded users Negative Rating ? Positive Rating 13.10.2012
  • 15. How collaborative filtering works?“People who liked this also liked…” abcd abcd How it works User-to-User  Recommendations are made by finding users with similar tastes. Jane and Tim both liked Item 2 and disliked Item 3; it seems they might have similar taste, which suggests that in general Jane agrees with Tim. This makes Item 1 a good recommendation for Tim. This approach does not scale well for millions of Item users. to Item Item-to-Item  Recommendations are made by finding items that have similar appeal to many users. Tom and Sandra are two users who liked both Item 1 and Item 4. That suggests that, in general, people who User to liked Item 4 will also like item 1, so Item 1 will be recommended to Tim. This approach is scalable to User millions of users and millions of items. 13.10.2012
  • 16. KNN - NEAREST NEIGHBOR Current User Users 1 1st item rate0 Dislike ? 1 01 Like abcd Prediction abcd Unknown Rating abcd Other Users 1 This user did not   There are other Items? 1  The prediction Unknown rate the item. We was made based users who rated 0 will try to predict a on the nearest the same item. rating according We are interested 1 neighbor. to his neighbors. in the Nearest abcdHamming Distance 1  The Hamming distance Neighbors. is named after Richard Hamming. 0 User Model = 1  In information theory, the Hamming distance between two strings of interaction abcd Nearest Neighbors equal length is the number of  We are looking 1 positions at which the corresponding abcd history the Nearest 1 symbols are different. for Neighbor. The Nearest  one with the 1 Neighbor lowest Hamming 0 14th item rate distance. Hamming 5 6 6 5 4 8 distance 13.10.2012
  • 17. IMPORTANT ISSUES Cold Start Implicit/Explicit Rating Sparsity  Long Tail problem - many items in the Long Tail have only few ratings Portfolio Effect: Non Diversity Problem  It is not useful to recommend all movies by Antonio Banderas to a user who liked one of them in the past Beyond Popularity  Gray sheep problem Iformation Security  Misuse  Privacy
  • 18. CONTENT-BASED RECOMMENDERSYSTEM
  • 19. CONTENT-BASED RECOMMENDATION In content-based recommendations the system tries to recommend items that matches the User Profile. The Profile is based on items user has liked in the past or explicit interests that he defines. A content-based recommender system matches the profile of the item to the user profile to decide on its relevancy to the user.
  • 20. SIMPLE EXAMPLE Read update User ProfileNew books Match User Profile Recommender Systems recommendation
  • 21. CONTEXT-BASED RECOMMENDERSYSTEMS
  • 22. Context-Based Recommender Systems abcd Overview  The recommender system uses additional data about the context of an item consumption.  For example, in the case of a restaurant the time or the location may be used to improve the recommendation compared to what could be performed without this additional source of information.  A restaurant recommendation for a Saturday evening when you go with your spouse should be different than a restaurant recommendation on a workday afternoon when you go with co-workers 13.10.2012
  • 23. Context-Based Recommender Systems Motivating Examples  Recommend a vacation  Winter vs. summer  Recommend a purchase (e-retailer)  Gift vs. for yourself  Recommend a movie  To a student who wants to watch it on Saturday night with his girlfriend in a movie theater. 13.10.2012
  • 24. Context-Based Recommender Systems Motivating Examples  Recommend music  The music that we like to hear is greatly affected by a context, such that can be thought of a mixture of our feelings (mood) and the situation or location (the theme) we associate it with.  Listen to Bruce Springteen "Born in USA" while driving along the 101.  Listening to Mozarts Magic Flute while walking in Salzburg. 13.10.2012
  • 25. Information Discovery: Example“Tell me the music that I want to listen NOW" abcd abcd Musicovery.com Details  An Interactive personalized WebRadio  A mood matrix propose a relationship between music and mood.  Ethnographic studies have shown that people choose music peaces according to their mood or mood change expectation. 13.10.2012
  • 26. Context-Based Recommender Systems What simple recommendation techniques ignore?  What is the user when asking for a recommendation?  Where (and when) the user is ?  What does the user (e.g., improve his knowledge or really buy a product)?  Is the user or with other ?  Are there products to choose or only ? 13.10.2012
  • 27. Context-Based Recommender Systems What simple recommendation techniques ignore?  What is the user when asking for a recommendation?  Where (and when) the user is ?  What does the user (e.g., improve his knowledge or really buy a product)?  Is the user or with other ?  Are there products to choose or only ? Plain recommendation technologies forget to take into account the user context. 13.10.2012
  • 28. Context-Based Recommender Systems abcd Major obstacle for contextual computing  Obtain sufficient and reliable data describing the user context  Selecting the right information, i.e., relevant in a particular personalization task  Understand the impact of contextual dimensions on the personalization process  Computational model the contextual dimension in a more classical recommendation technology  For instance: how to extend Collaborative Filtering to include contextual dimensions? 13.10.2012
  • 29. Context-Based Recommender Systems abcd Item Split - Intuition and Approach  Each item in the data base ( ) is a candidate for splitting  Context defines ( ) all possible splits of an item ratings vector  We test all the possible splits – we do not have many contextual features  We choose one split (using a single contextual feature) that maximizes an impurity measure and whose impurity is higher than a threshold 13.10.2012
  • 30. SOCIAL (TRUST) BASEDRECOMMENDER SYSTEMS
  • 31. Social Based (Trust based) Recommender Systems abcd Overview  Intuition – Users tend to receive advice from people they trust, i.e., from their friends.  Trusted friends can be defined explicitly by the users or inferred from social networks they are registered to. . 13.10.2012
  • 32. TRUST- BASED COLLABORATIVE FILTERING Active users’ trusted friends Active user 3 ? Rating prediction
  • 33. TRUST METRICS  Global metrics: computes a single global trust value for every single user (reputation on the network) b  Pros: d a  Based on the whole community opinion c  Cons:  Trust is subjective (controversial users)
  • 34. TRUST METRICS (CONT.) Local metrics: predicts (different) trust scores that are personalized from the point of view of every single user Pros:  More accurate  Attack resistance Cons:  Ignoring the “wisdom of the crowd” b a d c
  • 35. SEARCH ENGINES ANDRECOMMENDER SYSTEMS
  • 36. SEARCH ENGINES VS. RECOMMENDER SYSTEMS – Search Engines Recommender Systems Goal – answer users ad  Goal – recommend services hoc queries or items to user Input – user ad-hoc need  Input - user preferences defined as a query defined as a profile Output- ranked items relevant to user need  Output - ranked items based (based on her on her preferences preferences???) Methods - Mainly IR  Methods – variety of based methods methods, IR, ML, UM
  • 37. NEW TRENDS …  “Understand” the user actual needs from her context  Personalize results according to the user preferences Search engines may use some recommender systems methods to achieve these goals
  • 38. SEARCH ENGINES PERSONALIZATION METHODSADOPTED FROM RECOMMENDER SYSTEMS Collaborative filtering  User-based - Cross domain collaborative filtering is required??? Content-based  Search history Collaborative content-based  Collaborate on similar queries Context-based  Little research – difficult to evaluate  Locality, language, calendar Social-based  Friends I trust relating to the query domain  Notion of trust, expertise