Using Contextual Information as Virtual Items on Top-N Recommender Systems

TL;DR

This paper introduces a method to incorporate contextual information into top-N recommender systems by representing context as virtual items, enhancing recommendation relevance without altering existing algorithms.

Contribution

The paper proposes a novel approach to integrate context into recommender systems as virtual items, tested on multiple algorithms and datasets.

Findings

Method effectively leverages context when informative

Improves recommendation quality with added context

Compatible with existing algorithms

Abstract

Traditionally, recommender systems for the Web deal with applications that have two dimensions, users and items. Based on access logs that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a method to complement the information in the access logs with contextual information without changing the recommendation algorithm. The method consists in representing context as virtual items. We empirically test this method with two top-N recommender systems, an item-based collaborative filtering technique and association rules, on three data sets. The results show that our method is able to take advantage of the context (new dimensions) when it is informative.