large scale collaborative filtering using Apache Giraph
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This document discusses large scale collaborative filtering using Apache Giraph. It describes neighborhood-based models and matrix factorization for collaborative filtering. It also details how these techniques were implemented and optimized in Giraph to provide recommendations for billions of Facebook users and ratings. Key optimizations included a rotational approach for matrix factorization to reduce network traffic and memory usage for skewed item degrees.
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large scale collaborative filtering using Apache Giraph
- 1. Maja Kabiljo & Aleksandar Ilic, Facebook Large scale Collaborative Filtering using Apache Giraph
- 2. Conclusion 04 01 02 05 03 What is Apache Giraph? Collaborative Filtering problem Neighborhood-based models Matrix factorization
- 4. What is Apache Giraph? Iterative and graph processing on massive datasets Billion vertices, trillion edges Data mapped to a graph •Vertex ids and values •Edges and edge values “Think like a vertex” 10 5 1 3
- 5. What is Apache Giraph? Runs on top of Hadoop Map only jobs Keeps data in memory Mappers communicate through network
- 6. Giraph workflow Worker 1 Worker 2 Worker 3
- 8. Collaborative Filtering Predict user’s interests based on many other users Disney Roller coasters Disneyland Six Flags
- 9. Collaborative Filtering Main challenge: Facebook data •Billion users, 100 billion ratings •Skewed item degrees •No explicit ratings Common approaches: •Neighborhood based models •Matrix factorization
- 11. Neighborhood based CF Start from user item ratings Calculate item similarities For each item pair: •Users who rated first item •Users who rated second item •Users who rated both items ? u u u u u u I1 I2
- 12. Neighborhood based CF Calculate user recommendations For every user: •Items rated by user •Most similar items to these items ? ? ? ? I4 I5 I6 I7 I1 I2 I3 u
- 13. Configurable formulas Accommodating different use cases Each calculation step is configurable •User’s contribution to item similarities •Item similarities based on all user’s contributions •User to item recommendation score Passing a piece of Java code through configuration intersection / Math.sqrt(degree1 * degree2)
- 14. Users to items edges Preprocessing: •Filter out low degree ones •Calculate global item stats Users send item lists to items •Items need other items’ global stats to calculate similarities Worker 1 Worker 2 Worker 3 Our solution i u u u u i i iu
- 15. Optimizations Make item info globally available •Using reduce/broadcast api Striping technique •Split computation across multiple supersteps •In each stripe process one subset of items
- 16. Applications Direct user recommendations Context aware recommendations User explore
- 17. Item similarities implemented using Hive join •Remapping all items to 1..N first Comparison with Hive 150M users 15M items 4B ratings 1.3B users 35M items 15B ratings 2.4B users 8M items 220B ratings Hive CPU hours (after int remapping) 10 227 963 Giraph CPU hours 3 16 87
- 18. Ease of use ratings = i2iRatings(table = ‘user_item_ratings') similarities = i2iSimilarities(table = 'item_similarities') recommendations = i2iRecommendations(table = 'user_recommendations') i2iCalculateSimilarities(ratings, similarities, similarity_formula = '...', num_workers = 10) i2iCalculateRecommendations(ratings, similarities, recommendations, scoring_formula = '...', num_workers = 50)
- 20. ? ? ? ? ? ? ? ? ? Matrix factorization CF 4 4 1 3 5 3 1 1 2 4 5 3 4 5 2 3 ... . . . ... U1 U2 U3 U4 users ... U5 . . .I1 I2 I3 I4 items I5 ?
- 21. Basic form Objective function Two iterative approaches: •Stochastic Gradient Descent •Alternating Least Squares regularization
- 22. Standard approach A bipartite graph: •Users and items are vertices •Known ratings are edges •Feature vectors sent through edges Problems: •Data sent per iteration: #knownRatings * #features •Memory •Large degree items •SGD modifications are different than in the sequential solution Worker 1 Worker 2 Worker 3 I2 I1 I3 I4
- 23. Our solution Extending Giraph •Worker data •Worker to worker messages Users are vertices, items are worker data
- 24. Our solution - rotational approach Worker 1 Worker 2 Worker 3 item set 3 item set 1 item set 2 •Network traffic? •Memory? •Skewed item degrees? •SGD calculation?
- 25. Recommendations Finding top inner products Each (user, item) pair is unfeasible Creating Ball Tree from item vectors •Greedy tree traversal •Pruning subtrees •100-1000x faster
- 26. Additional features Tracking rmse, average rank and precision/recall Combining SGD & ALS Using other objective functions •CF for implicit feedback •Biases •Degree based regularization •Optimizing ranks
- 27. Applications Add user and item feature vectors in ranking Get user to item score in realtime Direct user recommendations
- 28. Training / testing metrics exampleRMSE 0 0.2 0.4 0.6 0.8 Iterations 0 4 8 12 16 20 24 28 32 36 40 44 Train f=8 Test f=8 Train f=128 Test f=128
- 29. Comparison with Spark MLlib Performance of Spark MLlib ALS CF published in July 2014 On scaled copies of Amazon reviews datasetCpuminutes 0 150 300 450 600 Millions examples 0 300 600 900 1200 Standard (in Spark) Rotational (in Giraph)
- 30. Ease of use ratings = CFRatings(table = 'cf_ratings') feature_vectors = CFFeatureVectors(table = 'cf_feature_vectors') CFTrain(ratings, feature_vectors, CFSettings(features_size = 10, iterations = 20), num_workers = 5) CFRecommend(ratings, feature_vectors, CFRecommendations(top_items_table = 'cf_top_items'), num_workers = 50)
- 31. Conclusion
- 32. Conclusion Scalable implementation of Collaborative Filtering On top of Apache Giraph Highly performant (>100 billion ratings) Neighborhood-based models Matrix factorization Group and Page recommendations at Facebook