BEARS: Towards an evaluation framework for bandit-based interactive recommender systems

Barraza-Urbina, Andrea , Koutrika, Georgia , d‘Aquin, Mathieu , & Hayes, Conor (2018). BEARS: Towards an evaluation framework for bandit-based interactive recommender systems. Paper presented at the REVEAL’18, Vancouver, Canada, 06-07 October, DOI: 10.13025/x72s-8r20

Abstract

Recommender Systems (RS) deployed in fast-paced dynamic scenarios must quickly learn to adapt in response to user evaluative feedback. In these settings, the RS faces an online learning problem where each decision should optimize two competing goals: gather new information about users and optimally serve users according to acquired knowledge. Related works commonly address this exploration-exploitation trade-off by proposing bandit-based RS. However, evaluating bandit-based RS in an offline interactive environment remains an open challenge. This paper presents BEARS, an evaluation framework that allows users to easily test bandit-based RS solutions. BEARS aims to support reproducible offline evaluations by providing simple building blocks for constructing experiments in a shared platform. Moreover, BEARS can be used to share benchmark problem settings (Environments) and reusable implementations of baseline solution approaches (RS Agents).

Funder

Science Foundation Ireland
European Regional Development Fund

Publisher

NUI Galway

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

Data Science Institute (Workshop Papers)