Incorporating Recklessness to Collaborative Filtering based Recommender Systems

TL;DR

This paper introduces a novel 'recklessness' measure in matrix factorization recommender systems to control risk and enhance prediction quality and quantity.

Contribution

It proposes incorporating a variance-based recklessness term into the learning process to regulate risk and improve recommendation performance.

Findings

Recklessness enables risk regulation in recommendations.

Inclusion of recklessness improves prediction quality.

Experimental results show increased prediction quantity and accuracy.

Abstract

Recommender systems are intrinsically tied to a reliability/coverage dilemma: The more reliable we desire the forecasts, the more conservative the decision will be and thus, the fewer items will be recommended. This causes a detriment to the predictive capability of the system, as it is only able to estimate potential interest in items for which there is a consensus in their evaluation, rather than being able to estimate potential interest in any item. In this paper, we propose the inclusion of a new term in the learning process of matrix factorization-based recommender systems, called recklessness, that takes into account the variance of the output probability distribution of the predicted ratings. In this way, gauging this recklessness measure we can force more spiky output distribution, enabling the control of the risk level desired when making decisions about the reliability of a prediction. Experimental results demonstrate that recklessness not only allows for risk regulation but also improves the quantity and quality of predictions provided by the recommender system.