A Solution to the Cold-Start Problem in Recommender Systems Based on Social Choice Theory

Abstract

Recommender systems are a popular approach for dealing with the problem of product overload. Collaborative Filtering (CF), probably the best known technique for recommender systems, is based on the idea of determining and locating like-minded users. However, CF suffers from a common phenomenon known as the cold-start problem, which prevents the technique from effectively locating suggestions for new users. In this paper we will investigate how to provide a recommendation to a new user, based on a previous group of users opinions, by utilizing techniques from social choice theory. Social choice theory has developed models for aggregating individual preferences and judgments, so as to reach a collective decision. We then determined how these can best be utilized to establish a collective decision as a recommendation for new users; hence, a solution to the cold start problem. This solution not only solves the cold-start problem, but can also be used to give existing users more accurate suggestions. We focused on models of preference aggregation and judgment aggregation; specifically, by using the judgment aggregation model to solve the cold-start problem, which is a novel approach.