A hybrid CBR approach for the long tail problem in recommender systems

Abstract

Recommender systems is an important tool to help users find relevant items to their interests in a variety of products and services including entertainment, news, research articles, and others. Recommender systems generate lists of recommendations/suggestions based on information from past user interactions, choices, demographic information as well as using machine learning and data mining. The most popular techniques for generating recommendations are through content-based and collaborative filtering with the latter used to provide user to user recommendations. However, collaborative filtering suffers from the long tail problem, i.e., it does not work correctly with items that contain a small number of ratings over large item populations with respectively large numbers of ratings. In this paper, we propose a novel approach towards addressing the long tail recommendation problem by applying Case-based Reasoning on “user history” to predict the rating of newly seen items which seem to belong to the long tail. We present a hybrid approach and a framework implemented with jCOLIBRI to evaluate it using the freely available Movielens dataset [8]. Our results seem promising and they seem to improve the existing prediction outcomes from the available literature.