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
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Map Reduce
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Collaborative Filtering
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Content Based Recommendation
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Clustering Techniques - Pros/Cons
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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 = { u 1 ,u 2 ,...,u N } ) over M items ( S = {s 1 ,s 2 ,...,s M } ), and given a specific user ‘ u ’ with click history set
C u consisting of stories { s i1 , . . . , s i|Cu | } , recommend K stories to the user.
Strict timing constraints for recommendation engine to generate recommendations.
Google News
Personalization
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Collaborative Clustering
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Probabilistic Latent Semantic Indexing
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Covisitation Counts
Google News
Personalization
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Record User Queries and Clicks
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Recommendations of News using user click history and click history of the community
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Google News
Personalization
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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.
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‣ Model-based Algorithms
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‣ Model the users based on their past ratings and use these models to predict ratings of unseen items.
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Mix of memory based + model based systems
Google News
Personalization
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Model based approach
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Clustering Techniques: Probabilistic
Latent Semantic Indexing(PLSI) and min hash
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Memory based approach
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Item Covisitation
Google News
Personalization
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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.
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Similarity calculated using Jaccard Coefficient
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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
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Locality Sensitive Hashing (LSH) comes for rescue
Google News
4/18/13
Personalization
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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.
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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.
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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)
Google News
Personalization
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Probabilistic Latent Semantic models
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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
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A hidden variable Z is used to define the relationship, it represents user communities and item communities.
Google News
Personalization
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Covisitation is defined as event in which two stories are clicked by same user within a
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A graph whose nodes represent items and weighted edges represent time discounted number of covisitation instances.
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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.
Google News
Personalization
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Fetch user u-i ’s recent click history - limited to past few hours or days.
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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.
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The value stored in entry normalized by sum of all entries for s-i is stored to recommendation score.
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Recommendation score is normalized to a value between 0 and 1 by linear scaling.
Google News
Personalization
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Three Components:
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Offline component to cluster users based on click history
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Online servers:
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Updating story and user statistics each time user clicks on news story
• Generating news story when user requests
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Two Data Tables
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User Table (UT) indexed by user-id, stores user click history and clustering information.
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• Story Table (ST) indexed by story-id, stores real time click counts for every storystory and story-cluster pair.
Google News
Personalization
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NSS f f b u e r
UT user click
NFE view personalized news page request
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NPS c h c a e
Fetch
NFE: News Front End
NSS: News Statistics Server
NPS: News Personalization
Server
Google News
Personalization
Update Statistics
ST
Statistics
UT: User Table
ST: Story Table
Min Hashing
PLSI
Offline
Log
Analysis
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Scalable collaboration of Content based and Collaborative clustering
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Recommendation system using Min
Hashing and PLSI Algorithms
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Scaling the algorithms by using Map
Reduce and Big Table representation for data
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Using click events as vote for news
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Dynamically providing the latest likely news that suits the interest of the user
Google News
Personalization

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Depends a lot on User Clicks
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User Clicks considered as positive vote
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Does not say anything about negative vote
Google News
Personalization
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