Google News Personalization Scalable Online Collaborative Filtering Abhinandan Das - abhinandan@google.com Mayur Datar - mayur@google.com Ashutosh Garg - ashutosh@google.com Shyam Rajaram - rajaram1@ifp.uiuc.edu Presented by: Aniket Zamwar - zamwar@usc.edu

Already Studied in Class Map Reduce Collaborative Filtering Content Based Recommendation Clustering Techniques - Pros/Cons

Problem Statement Scale of operation is very huge - order of several million news stories dynamically changing at high rate. Presented with the click history for N users ( U = { u1,u2,...,uN} ) over M items ( S = {s1,s2,...,sM} ), and given a specific user ‘u’ with click history set Cu consisting of stories { si1 , . . . , si|Cu | }, recommend K stories to the user. Strict timing constraints for recommendation engine to generate recommendations. 4/18/13 Google News Personalization

Approaches Collaborative Clustering Probabilistic Latent Semantic Indexing Covisitation Counts 4/18/13 Google News Personalization

Problem Setting Record User Queries and Clicks Recommendations of News using user click history and click history of the community 4/18/13 Google News Personalization

Recommender System Content based Systems Collaborative Filtering Systems Memory-based Algorithms Prediction calculated as weighted average of the ratings given by other users weight is proportional to to “similarity” between users. Model-based Algorithms Model the users based on their past ratings and use these models to predict ratings of unseen items. Mix of memory based + model based systems 4/18/13 Google News Personalization

Algorithms Model based approach Clustering Techniques: Probabilistic Latent Semantic Indexing(PLSI) and min hash Memory based approach Item Covisitation 4/18/13 Google News Personalization

Min Hashing Probabilistic clustering technique - assigns pairs of users to same cluster with probability proportional to overlab between the set of items the users have voted for. Similarity calculated using Jaccard Coefficient To Do: Given user u-i, compute similarity S(u-i, u-j) for all users u-j, and recommend stories to u-i voted by u-j with weight equal to S(u-i, u-j) Issues: Real time not scalable, using hash table to find vote for specific user is also not feasible, offline computation is also not feasible Locality Sensitive Hashing (LSH) comes for rescue 4/18/13 Google News Personalization

Locality Sensitive Hashing Key Idea: Hash data points using several hash functions, such that for each hash function the probability of collision is much higher for objects which are close to each other. Min-hashing technique is used to randomly permute the set of items (S) and for each user u-i compute its hash value h(u-i) as the index of first item under the permutation that belongs to user’s item set Cu-i. Min-hashing = probabilistic clustering where each hash bucket corresponds to a cluster, that puts two users together in the same cluster with probability equal to item set similarity S(u-i, u-j) 4/18/13 Google News Personalization

PLSI Probabilistic Latent Semantic models Models users and items as random variables - relationship between users and items is learned by modeling joint distribution of users and items as mixed distribution A hidden variable Z is used to define the relationship, it represents user communities and item communities. 4/18/13 Google News Personalization

Covisitation Covisitation is defined as event in which two stories are clicked by same user within a certain time interval. A graph whose nodes represent items and weighted edges represent time discounted number of covisitation instances. For each user click the adjacency list representing graph is updated: for entry for each item in user history, new entry corresponding to clicked item is added if not there; if it is already there then the age discounted count is updated. 4/18/13 Google News Personalization

Covisitation based Recommendation Fetch user u-i’s recent click history - limited to past few hours or days. For each item s-i in click history of user, lookup the entry for pair (s-i, s) in adjacency list for s-i stored in Big Table. The value stored in entry normalized by sum of all entries for s-i is stored to recommendation score. Recommendation score is normalized to a value between 0 and 1 by linear scaling. 4/18/13 Google News Personalization

System Setup Three Components: Offline component to cluster users based on click history Online servers: Updating story and user statistics each time user clicks on news story Generating news story when user requests Two Data Tables User Table (UT) indexed by user-id, stores user click history and clustering information. Story Table (ST) indexed by story-id, stores real time click counts for every story- story and story-cluster pair. 4/18/13 Google News Personalization

Clusters + Click History System Components NSS Min Hashing PLSI Clusters + Click History b u f e r UserID + Clicked Story UT UserId + Clicked Story User Clusters user click NFE Offline Log Analysis User Click Histories UserID + Candidate Stories Clusters + Click History view personalized news page request Update Statistics ST NPS Ranked Stories Fetch Statistics c a h e NFE: News Front End NSS: News Statistics Server NPS: News Personalization Server UT: User Table ST: Story Table 4/18/13 Google News Personalization

Pros Scalable collaboration of Content based and Collaborative clustering Recommendation system using Min Hashing and PLSI Algorithms Scaling the algorithms by using Map Reduce and Big Table representation for data Using click events as vote for news Dynamically providing the latest likely news that suits the interest of the user 4/18/13 Google News Personalization

Cons Depends a lot on User Clicks User Clicks considered as positive vote Does not say anything about negative vote 4/18/13 Google News Personalization