1. RecStore An Extensible and Adaptive Framework for Online Recommender Queries inside the Database Engine

2. Microsoft Research: – Justin J. Levandoski University of Minnesota: – Mohamed Sarwat – Mohamed F. Mokbel – Michael D. Ekstrand 2 Authors

3. Recommender Systems – Basic Idea Users: provide opinions on items consumed/watched/listened to… The system: provides the user suggestions for new items 3

4. Analyze user behavior to recommend users personalized and interesting things to do/read/see rate movies Movie Ratings build recommendation model Similar Users Similar Items recommendation query “Recommend user A five movies” Offline Online Recommender Systems – Basic Idea 4

5. Things have changed ! We live in an increasingly social and “real-time” world – Number of things to recommend is growing exponentially – Users expressing opinions faster than ever – Recommendations change second-to-second “Offline” step can no longer be tolerated “Like” button NY Times “Recommend” button Facebook Posts Blog/News Items 5

6. No work has explored recommender system performance – Performance has always been synonymous with “quality” “[Our] solution is based on a huge amount of models and predictors which would not be practical as part of a commercial recommender system. However, this result is a direct consequence of the nature and goal of the competition: obtain the highest possible accuracy at any cost, disregarding completely the complexity of the solution and the execution performance." Team BelKor’s Pragmatic Chaos Winner of the 2009 Netflix Prize Herlocker et al. “Evaluating Collaborative Filtering Recommender Systems”, ACM TOIS 2004 “We have chosen not to discuss computation performance of recommender algorithms. Such performance is certainly important, and in the future we expect there to be work on the quality of time- limited and memory-limited recommendations.” Existing Recommender Systems 6

7. Incoming stream of rating data: (user, item, rating) Ratings are used to build a recommendation model as: – Item-based collaborative filtering: (item, item, similarity) – User-based collaborative filtering: (user, user, similarity) Recommendation query: – Item-based collaborative filtering: Given a user u, find the top-k items that are most similar to the items that u has liked before – User-based collaborative filtering: Given a user u, find the top-k items that the users who are similar to u have liked “Online” recommendation environments have all the pieces of a data management problem Recommender Systems in DBMS 7

8. RecStore Main Idea RecStore System Architecture RecStore System Features RecStore Experimental Results Conclusion Talk Outline 8

9. RecStore Main Idea RecStore System Architecture RecStore System Features RecStore Experimental Results Conclusion Talk Outline 9

10. Lets NOT try to find a new way of doing recommendation* * ACM RecSys community is already doing excellent job in this frontier. Lets start from there. RecStore – Main Idea RecStore pushes the Recommender Systems inside the Database Engine to provide online support and scale up the computations of existing recommender methods. 10

11. RecStore Main Idea RecStore System Architecture RecStore System Features RecStore Experimental Results Conclusion Talk Outline 11

12. RecStore – System Architecture Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Updates Recommendation Queries Access Methods (Index, Scan) 1 1 2 2 3 3 FAST MEDIUM SLOW MEDIUM FAST 12

13. RecStore Main Idea RecStore System Architecture RecStore System Features RecStore Experimental Results Conclusion Talk Outline 13

14. – Adaptivity: RecStore is adaptive to different system workloads (Query Intensive Vs. Update Intensive) RecStore – System Features 14 – Extensibility: RecStore is extensible to support many recommendation methods (e.g., item- based CF, user-based CF).

15. RecStore – Adaptivity (1/6) Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Update Recommendation Queries Access Methods (Index, Scan) 1 1 2 2 3 3 - Low Latency Recommendation Query. -High Storage and maintenance Cost. Materialize-All (α = β = M) α β 15

16. RecStore – Adaptivity (2/6) Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Update Recommendation Queries Access Methods (Index, Scan) 1 1 2 2 3 3 - High Latency Recommendation Query -Low Storage and maintenance Cost. Materialize-None (α = β = 0) α β 16

17. RecStore – Adaptivity (3/6) Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Update Recommendation Queries Access Methods (Index, Scan) 1 1 2 2 3 3 - Middle Ground between Materialize-All and Materialize-None Intermediate Store Only (α = M, β = 0) α β 17

18. RecStore – Adaptivity (4/6) Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Update Recommendation Queries Access Methods (Index, Scan) 1 1 2 2 3 3 - Middle Ground between Materialize-All and Intermediate-Only Full Intermediate Store / Partial Model Store (α = M, β = N) α β 18 N N

19. RecStore – Adaptivity (5/6) Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Update Recommendation Queries Access Methods (Index, Scan) 1 1 2 2 3 3 - Lies between Partial Model and Intermediate Only Partial Intermediate Store / Partial Model Store (α = K, β = N) α β 19 N N K K

20. Intermediate Store Only (α = M, β = 0) Full Intermediate Store / Partial Model Store (α = M, β = N) RecStore – Adaptivity (6/6) Model Filter Intermediate Filter Rating Data Model Table Intermediate Store Rating Update - Low Latency Recommendation Query. -High Storage and maintenance Cost. Materialize-All (α = β = M) - High Latency Recommendation Query -Low Storage and maintenance Cost. Materialize-None (α = β = 0) - Middle Ground between Materialize-All and Materialize-None - Middle Ground between Materialize-All and Intermediate-Only - Lies between Partial Model and Intermediate Only Partial Intermediate Store / Partial Model Store (α = K, β = N) α β 20

21. RecStore is Extensible to support various Recommendation Methods 21 User-based CF Item-based CF (Cosine) MyRec RecStore DBMS Item-based CF (Pearson) Item-based CF (Probabilistic) RecStore – Extensibility The Application Developer can define a new recommendation method using SQL code The recommendation method is registered using the SQL clause: Define RecStore Model

22. RecStore – Extensibility DEFINE RECSTORE MODEL ItemItemCosine FROMRatings R1, Ratings R2 WHERER1.ItemId <> R2.itemId AND R1.userId = R2.userId WITH INTERMEDIATE STORE: (R1.itemID as item, R2.itemId as rel_itm, vector_lenp, vector_lenq, dot_prod, co_rate) WITH INTERMEDIATE FILTER: ALLOW UPDATE WITH My_IntFilterLogic(), UPDATE vector_lenp AS vector_lenp + R1.rating * R1.rating, UPDATE vector_lenq AS vector_lenp + R2.rating * R2.rating, UPDATE dot_prod AS ot_prod + R1.rating * R2.rating, UPDATE co_rate AS 1 WITH MODEL STORE: (R1.itemId as item, R2.itemId as rel_itm, COMPUTED sim) WITH MODEL FILTER: ALLOW UPDATE WITH My_ModFilterLogic(), UPDATE sim AS if (co_rate < 50) co_rate * dot_prod / ( 50*sqrt(vector_lenp) * sqrt (vector_lenq)); else co_rate / sqrt(vector_lenp) * sqrt(vector_lenp); 22 RecStore DBMS Item-based CF (Cosine) Simple SQL to Plug-in a new Recommendation Method Intermediate Stats Model Store

23. RecStore Main Idea RecStore System Architecture RecStore System Features RecStore Experimental Results Conclusion Talk Outline 23

24. RecStore – Experimental Evaluation (1/3) MovieLens Data 10 Million ratings 10k items, 70k users MovieLens Data 10 Million ratings 10k items, 70k users Machine – Intel Core2 8400 at 3Ghz with 4GB of RAM running Ubuntu Linux 8.04 Machine – Intel Core2 8400 at 3Ghz with 4GB of RAM running Ubuntu Linux 8.04 Techniques – matall: materialize all ( α = β = M ) – ionly: intermediate store only ( α = M and β = 0 ) – pm-m: partial model store ( α = M and β = 20% of all movies ) – pm-mi: partial model/partial intermediate ( α = 40 % and β = 20% of all movies ). – viewreg: Regular PostgreSQL view – viewmat: Simulated materialized view in Postgress Techniques – matall: materialize all ( α = β = M ) – ionly: intermediate store only ( α = M and β = 0 ) – pm-m: partial model store ( α = M and β = 20% of all movies ) – pm-mi: partial model/partial intermediate ( α = 40 % and β = 20% of all movies ). – viewreg: Regular PostgreSQL view – viewmat: Simulated materialized view in Postgress PostgreSQL 8.4 24

25. RecStore – Experimental Evaluation (2/3) Item-Based Cosine Similarity 25 RecStore is adaptive to a spectrum of workload ranging from query intensive workloads to update Intensive workload

26. RecStore – Experimental Evaluation (3/3) Real workload trace continuous arrival of both: -rating updates -recommender queries against the MovieLens System. Item-Based Cosine Similarity 26

27. RecStore Main Idea RecStore System Architecture RecStore System Features RecStore Experimental Results Conclusion Talk Outline 27

28. Conclusion: Wrap Up 28

29. Recommender Systems have all the ingredients of a data management problem. RecStore is a step to incorporate Recommender Systems in the database engine. RecStore is adaptive to different system workloads (queries vs. updates) RecStore is extensible to support new recommendation methods. 29 Conclusion: Take-Away Message

30. Questions? 30