1. Google News Personalization Big Data reading group November 12, 2007 Presented by Babu Pillai

2. Problem: finding stuff on Internet Know what you want: –content-based filtering, –search Don’t know –browse How to handle: Don’t know but, show me something interesting!

3. Google News Top Stories Recommendations for registered users Based on user click history, community clicks

4. Problem Scale Lots of users, (more is good) –Millions of clicks from millions of users Problem: high churn in item set –Several million items (clusters of news articles about the same story, as identified by GN) per month –Continuous addition, deletion Strict timing (few hundred ms) Existing systems not suitable

5. Memory-based Ratings General form: where r is rating of item s k for user u a, and w(u a,u i ) is similarity between users u a and u i Problem: scalability, even when similarity is computed offline

6. Model-based techniques Clustering / segmentation, e.g. based on interests Bayesian models, Markov Decision, … –All are computationally expensive

7. What’s in this paper? Investigate 2 different ways to cluster users: MinHash, and PLSI Implement both on MapReduce

8. Google News Rating Model 1 click = 1 positive vote Noisier than 1-5 ranking (Netflix) No explicit negatives Why might it work? Partly due to the fairly significant article clips provided, so a user that clicks is likely genuinely interested

9. Design guidelines for a scalable rating system Associate users into clusters of similar users (based on prior clicks, offline) Users can belong to multiple clusters Generate rating using much smaller sets of user clusters, rather than all users:

10. Technique 1: MinHash Probabilistically assign users to clusters based on click history Use Jaccard coefficient: distance is a metric Using this metric is computationally expensive, not feasible even offline

11. MinHash as a form of Locality Sensitive Hashing Basic idea: assign hash value to each use based on click history How: randomly permute set of all items; assign id of first item in this order that appears in the user’s click history as the hash value for the user Probability that 2 users have the same hash is equal to the Jaccard coefficient

12. Using MinHash for clusters Concatenate p>1 such hashes as cluster id for increased precision Apply q>1 in parallel (users belong to q clusters) to improve recall Don’t actually maintain p*q permutations: hash item id with random seed to get proxy for permutation index, for p*q different seeds

13. MinHash on MapReduce Generate p x q hashes for each user based on click history; generate q p-long cluster ids by concatenation Map using cluster id’s as keys Reduce to form membership lists for each cluster id

14. Technique 2: PLSI clustering Probabilistic Latent Semantic Indexing Main idea: hidden state z that correlates users and items Generate this clustering from training set based on EM algorithm give by Hoffman04 –Iterative technique, generates new probability estimates based on previous estimates

15. PLSI as MapReduce Q* can be independently computed for each (u,s), given prior N(z,s), N(z), p(z|u): map to RxK machines (R, K partitions for u, s respectively) Reduce is simply addition

16. PLSI in a dynamic environment Treat Z as user clusters On each click, update p(s|z) for all clusters the user belongs to This approximates PLSI, but is updated dynamically as additional items are added Does not allow additions of users

17. Cluster-based recommendation For each cluster, maintain number of clicks, decayed by time, for each item visited by a member For a candidate item, lookup user’s clusters, add up age-discounted visitation counts, normalized by total clicks Do this using both MinHash and PLSI clustering

18. One more technique: Covisitation Memory-based technique Create adjacency matrix between all pairs of items (can be directed) Increment corresponding count if one item visited soon after another Recommendation: for candidate item j, sum of all counts from i to j for all items i in recent click history of user, normalized appropriately

19. Whole System Offline clustering Online click history update, cluster item stats update, covisitation update

20. Results Generally around 30-50% better than popularity based recommendations

21. Techniques don’t work well together, though

22. Discussion Covisitation appears to work as well as clustering Operational details missing: how big are cluster memberships, etc. All of the clustering is done offline