Using Implicit Feedback for Neighbors Selection: Alleviating the Sparsity Problem in Collaborative Recommendation Systems

Abstract

The most popular Recommender systems (RSs) employ Collaborative Filtering (CF) algorithms where users explicitly rate items. Based on these ratings, a user-item rating matrix is generated and used to select the items to be recommended for a target user. An important step in this process is to determine the neighborhood of a target user, i.e. a set of users who rate items similarly to this user. One of the limitations of CF is precisely the need of rating data provided voluntarily by users. The lack of interest of users to provide this kind of information increases the sparsity problem of the ratings matrix. In this paper, we propose the use of implicit feedback for neighbors selection to alleviate the sparsity problem in CF-based RSs. In this proposal, user profiles are built based on the characteristics of items that have been accessed or purchased, and not necessarily rated by the users. This user profile is used exclusively to the neighborhoods formation, which considers not how they have rated items, but by the characteristics of the items that they have accessed or purchased. Our technique was implemented with Apache Mahout Framework and evaluated across experiments in the domain of movies by using a dataset from Movielens project. The results demonstrated that our technique produces better quality recommendations when compared to the classic CF mainly in presence of sparsity of rating data.