Recommendation with the Right Slice: Speeding Up Collaborative Filtering with Factorization Machines

We propose an alternative way to efficiently exploit rating data for collaborative filtering with Factorization Machines (FMs). Our approach partitions user-item matrix into ‘slices’ which are mutually exclusive with respect to items. The training phase makes direct use of the slice of interest (target slice), while incorporating information from other slices indirectly. FMs represent user-item interactions as feature vectors, and they offer the advantage of easy incorporation of complementary information. We exploit this advantage to integrate information from other auxiliary slices. We demonstrate, using experiments on two benchmark datasets, that improved performance can be achieved, while the time complexity of training can be reduced significantly.

• Type: Journal
• Year: 2015
• Where: RECSYS
• Authors: Babak Loni, Martha Larson, Alexandros Karatzoglou, Alan Hanjalic
• Keywords: Control Systems, RECSYS 2015