Recommender Systems by means of Information Retrieval

TL;DR

This paper proposes reformulating recommender systems as an information retrieval problem, using IR algorithms to predict user ratings, and demonstrates this approach with experimental comparisons.

Contribution

It introduces a novel approach to recommender systems by applying IR techniques, specifically reformulating user-movie interactions as IR queries and documents.

Findings

IR-based methods achieve comparable prediction accuracy to traditional approaches

Discrete Fourier Transform-based IR model shows promising results

Vector space IR model provides a baseline for comparison

Abstract

In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for some movies; we hide some values from the dataset, and we try to predict them again using its remaining portion (the so-called "leave-n-out approach"). In order to use an Information Retrieval algorithm, we reformulate this Recommender Systems problem in this way: a user corresponds to a document, a movie corresponds to a term, the active user (whose rating we want to predict) plays the role of the query, and the ratings are used as weigths, in place of the weighting schema of the original IR algorithm. The output is the ranking list of the documents ("users") relevant for the query ("active user"). We use the ratings of these users, weighted according to the rank, to predict the rating of the active user. We carry out the comparison by means of a typical metric, namely the accuracy of the predictions returned by the algorithm, and we compare this to the real ratings from users. In our first tests, we use two different Information Retrieval algorithms: LSPR, a recently proposed model based on Discrete Fourier Transform, and a simple vector space model.