Modeling user's receptiveness over time for recommendation

Abstract

Existing recommender systems model user interests and the social influences independently. In reality, user interests may change over time, and as the interests change, new friends may be added while old friends grow apart and the new friendships formed may cause further interests change. This complex interaction requires the joint modeling of user interest and social relationships over time. In this paper, we propose a probabilistic generative model, called Receptive-ness over Time Model (RTM), to capture this interaction. We design a Gibbs sampling algorithm to learn the receptive-ness and interest distributions among users over time. The results of experiments on a real world dataset demonstrate that RTM-based recommendation outperforms the state-of-the-art recommendation methods. Case studies also show that RTM is able to discover the user interest shift and re-ceptiveness change over time. Copyright © 2013 ACM.