CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering

Transcription

1 CCCF: Improving Collaborative Filtering via Scalable User-Item Co-Clustering Yao Wu 1, Xudong Liu 2, Min Xie 3 Martin Ester 1 and Qing Yang 2 Equal Contribution 1 Simon Fraser University 2 Institute of Automation, Chinese Academy of Sciences February 23, /8

2 Motivation Items Users 2/8

3 Overview of ology 1 Cluster users and items in to subgroups. We present a probabilistic method to learn overlapping co-clusters. We present a scalable inference algorithm using stochastic MCMC. 2 Collaborative Filtering models in subgroups. Any recommender model can be used. Easily parallelizable. 3 Aggregate recommendations from subgroups. We discuss several strategies for recommendation aggregation. 3/8

4 CCCF v.s. Basic CF method Motivation CCCF improves CF method by co-clustering. Table: CCCF vs. No Co-Clustering on the Netflix data POP ITEMCF MF WARP NONE CCCF NONE CCCF NONE CCCF NONE CCCF Table: CCCF vs. No Co-Clustering on the Yelp data POP ITEMCF MF WARP NONE CCCF NONE CCCF NONE CCCF NONE CCCF /8

5 CCCF v.s. Other Co-Clustering Methods CCCF outperforms other co-clustering methods POPITEMCF MF WARP BaseMethod POPITEMCF MF WARP BaseMethod CoClustering MCoC BBDF CCCF POPITEMCF MF WARP BaseMethod POPITEMCF MF WARP BaseMethod CoClustering MCoC BBDF CCCF (a) Results on the Netflix data (b) Results on the MovieLens data Figure: Comparison with other two co-clustering methods on the three data sets. 5/8

6 Qualitative Analysis on Yelp data: Items with largest weight in subgroups Sample Cluster 1 Sample Cluster 2 Sample Cluster 3 Business Name City Description Business Name City Description Business Name City Description Gallo Blanco Phoenix Restaurants Bread and Butter Henderson Restaurants North Premium Outlets Shopping Lux Phoenix Restaurants Snow Ono Orleans Hotel & Restaurants Shave Ice Casino Casinos & Hotels Postino Central Phoenix Restaurants Maizie s Cafe & Bistro Cherryblossom Noodle Cafe Buldogis Gourmet Hot Dogs Restaurants Phoenix Restaurants Sweet Tomatoes Restaurants Phoenix Restaurants Strip N Dip Chicken Strips Restaurants Pizza a Metro Phoenix Restaurants Patisserie Manon Restaurants SideBar Phoenix Bars Island Flavor Restaurants Churn Phoenix Coffee & Tea Japanese Curry Zen Restaurants Carly s Bistro Phoenix Restaurants Slidin Thru Restaurants Fremont Street Experience Conservatory & Botanical Garden Planet Hollywood Las Vegas Resort & Casino Flamingo Las Vegas Hotel & Casino New York - New York Miracle Mile Shops Palms Casino Resort The Forum Entertainment Entertainment Casinos & Hotels Casinos & Hotels Casinos & Hotels Shopping Casinos & Hotels 6/8 Federal Pizza Phoenix Yao Restaurants Wu (Simon Fraser ArtU) Flavors WSDM 16 CCCF: Improving FoodCollaborative Filtering via Scalable LasUser-Item Vegas Co-Clustering Shopping

7 Motivation We propose a new co-clustering method CCCF and a scalable inference algorithm. CCCF improves the performance of several popular Collaborative Filtering methods, and outperforms other co-clustering methods. 7/8

8 Thanks! yaowu.co 8/8