BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System

TL;DR

BanditMF introduces a novel multi-armed bandit based matrix factorization approach to improve recommender systems, effectively addressing cold start issues and user correlation challenges in dynamic environments.

Contribution

This paper presents BanditMF, a new method combining bandit algorithms with matrix factorization to enhance collaborative filtering in recommender systems.

Findings

Improves cold start recommendation accuracy.

Effectively models user correlations in recommendations.

Reduces sub-optimal recommendations in social domains.

Abstract

Multi-armed bandits (MAB) provide a principled online learning approach to attain the balance between exploration and exploitation. Due to the superior performance and low feedback learning without the learning to act in multiple situations, Multi-armed Bandits drawing widespread attention in applications ranging such as recommender systems. Likewise, within the recommender system, collaborative filtering (CF) is arguably the earliest and most influential method in the recommender system. Crucially, new users and an ever-changing pool of recommended items are the challenges that recommender systems need to address. For collaborative filtering, the classical method is training the model offline, then perform the online testing, but this approach can no longer handle the dynamic changes in user preferences which is the so-called cold start. So how to effectively recommend items to users in the absence of effective information? To address the aforementioned problems, a multi-armed bandit based collaborative filtering recommender system has been proposed, named BanditMF. BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering: (1) how to solve the cold start problem for collaborative filtering under the condition of scarcity of valid information, (2) how to solve the sub-optimal problem of bandit algorithms in strong social relations domains caused by independently estimating unknown parameters associated with each user and ignoring correlations between users.