1. Google News Personalization Scalable Online Collaborative Filtering
Abhinandan Das -
Mayur Datar -
Ashutosh Garg -
Shyam Rajaram -
Presented by: Aniket Zamwar -

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

3. 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

4. Approaches Collaborative Clustering
Probabilistic Latent Semantic Indexing
Covisitation Counts
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Google News Personalization

5. Problem Setting Record User Queries and Clicks
Recommendations of News using user click history and click history of the community
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Google News Personalization

6. 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
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Google News Personalization

7. Algorithms Model based approach
Clustering Techniques: Probabilistic Latent Semantic Indexing(PLSI) and min hash
Memory based approach
Item Covisitation
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Google News Personalization

8. 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
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Google News Personalization

9. 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)
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Google News Personalization

10. 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.
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Google News Personalization

11. 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

12. 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.
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Google News Personalization

13. 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.
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Google News Personalization

14. 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
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Google News Personalization

15. 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
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Google News Personalization

16. 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