Multi-Objective Ranked Bandits for Recommender Systems

Abstract

This paper is interested in recommender systems that work with implicit feedback in dynamic scenarios providing online recommendations, such as news articles and ads recommendation in Web portals. In these dynamic scenarios, user feedback to the system is given through clicks, and feedback needs to be quickly exploited to improve subsequent recommendations. In this scenario, we propose an algorithm named multi-objective ranked bandits, which in contrast with current methods in the literature, is able to recommend lists of items that are accurate, diverse and novel. The algorithm relies on four main components: a scalarization function, a set of recommendation quality metrics, a dynamic prioritization scheme for weighting these metrics and a base multi-armed bandit algorithm. Results show that our algorithm provides improvements of 7.8 and 10.4% in click-through rate in two real-world large-scale datasets when compared to the single-objective state-of-the-art algorithm.