Improved personalized rankings using implicit feedback

Abstract

Most users give feedback through a mixture of implicit and explicit information when interacting with websites. Recommender systems should use both sources of information to improve personalized recommendations. In this paper, it is shown how to integrate implicit feedback information in form of pairwise item rankings into a neural network model to improve personalized item recommendations. The proposed two-sided approach allows the model to be trained even for users where no explicit feedback is available. This is especially useful to alleviate a form of the new user cold-start problem. The experiments indicate an improved predictive performance especially for the task of personalized ranking.