Note to other teachers and users of these slides We would be delighted if you found this our material useful in giving your own lectures Feel free to use these slides verbatim or to modify them to fit your own needs If you make use of a significant portion of these slides in your own lecture please include this message or a link to our web site http www mmds org Recommender Systems Content based Systems Collaborative Filtering Mining of Massive Datasets Jure Leskovec Anand Rajaraman Jeff Ullman Stanford University http www mmds org High Dimensional Data Infinite data Machin e learnin g Apps PageRank SimRank Filtering data streams SVM Recommen der systems Clustering Community Detection Web advertising Decision Trees Association Rules Dimensional ity reduction Spam Detection Queries on streams Perceptron kNN Duplicate document detection High dim data Graph data Locality sensitive hashing J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 2 Example Recommender Systems Customer X Buys Metallica CD Buys Megadeth CD Customer Y Does search on Metallica Recommender system suggests Megadeth from data collected about customer X J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 3 Recommendations Examples Search Recommendations Items Products web sites blogs news items J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 4 From Scarcity to Abundance Shelf space is a scarce commodity for traditional retailers Also TV networks movie theaters Web enables near zero cost dissemination of information about products From scarcity to abundance More choice necessitates better filters Recommendation engines How Into Thin Air made Touching the Void a bestseller http www wired com wired archive 12 10 tail html J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 5 Sidenote The Long Tail Source Chris Anderson 2004 J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 6 Physical vs Online Read http www wired com wired archive 12 10 tail html to learn more J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 7 Types of Recommendations Editorial and hand curated List of favorites Lists of essential items Simple aggregates Top 10 Most Popular Recent Uploads Tailored to individual users Amazon Netflix J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 8 Formal Model X set of Customers S set of Items Utility function u X S R R set of ratings R is a totally ordered set e g 0 5 stars real number in 0 1 J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 9 Utility Matrix Avatar Alice 1 David Matrix 0 2 Pirates 0 2 0 5 Bob Carol LOTR 0 3 1 0 4 J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 10 Key Problems 1 Gathering known ratings for matrix How to collect the data in the utility matrix 2 Extrapolate unknown ratings from the known ones Mainly interested in high unknown ratings We are not interested in knowing what you don t like but what you like 3 Evaluating extrapolation methods How to measure success performance of recommendation methods J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 11 1 Gathering Ratings Explicit Ask people to rate items Doesn t work well in practice people can t be bothered Implicit Learn ratings from user actions E g purchase implies high rating What about low ratings J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 12 2 Extrapolating Utilities Key problem Utility matrix U is sparse Most people have not rated most items Cold start New items have no ratings New users have no history Three approaches to recommender systems 1 Content based Today 2 Collaborative 3 Latent factor based J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 13 Content based Recommender Systems Content based Recommendations Main idea Recommend items to customer x similar to previous items rated highly by x Example Movie recommendations Recommend movies with same actor s director genre Websites blogs news Recommend other sites with similar content J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 15 Plan of Action Item profiles likes build recommend match Red Circles Triangles User profile J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 16 Item Profiles For each item create an item profile Profile is a set vector of features Movies author title actor director Text Set of important words in document How to pick important features Usual heuristic from text mining is TF IDF Term frequency Inverse Doc Frequency Term Feature Document Item J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 17 Sidenote TF IDF fij frequency of term feature i in doc item j Note we normalize TF to discount for longer documents ni number of docs that mention term i N total number of docs TF IDF score wij TFij IDFi Doc profile set of words with highest TF IDF scores together with their scores J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 18 User Profiles and Prediction User profile possibilities Weighted average of rated item profiles Variation weight by difference from average rating for item Prediction heuristic Given user profile x and item profile i estimate J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 19 Pros Content based Approach No need for data on other users No cold start or sparsity problems Able to recommend to users with unique tastes Able to recommend new unpopular items No first rater problem Able to provide explanations Can provide explanations of recommended items by listing content features that caused an item to be recommended J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 20 Cons Content based Approach Finding the appropriate features is hard E g images movies music Recommendations for new users How to build a user profile Overspecialization Never recommends items outside user s content profile People might have multiple interests Unable to exploit quality judgments of other users J Leskovec A Rajaraman J Ullman Mining of Massive Datasets http www mmds org 21 Collaborative Filtering Harnessing quality judgments of other users Collaborative Filtering Consider user x Find set N of other x users whose ratings are similar to x s ratings N Estimate x s
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