School Seminars and Colloquia

A Group-Specific Recommender System

School Seminar

by Professor Annie Qu

Institution: University of Illinois
Date: Wed 22nd March 2017
Time: 10:00 AM
Location: Room 113, Doug McDonell Building

Abstract: In recent years, there has been a growing demand to develop efficient recommender systems which track users’ preferences and recommend potential items of interest to users. In this talk, we propose a group-specific method to utilize dependency information from users and items which share similar characteristics under the singular value decomposition framework.

The new approach is effective for the “cold-start” problem, where, in the testing set, majority responses are obtained from new users or for new items, and their preference information is not available from the training set. One advantage of the proposed model is that we are able to incorporate information from the missing mechanism and group-specific features through clustering based on the numbers of ratings from each user and other variables associated with missing patterns.

Our simulation studies and MovieLens data analysis both indicate that the proposed group-specific method improves prediction accuracy significantly compared to existing competitive recommender system approaches. In addition, we also extend the recommender system for the tensor data with multiple arrays.