Item-centric Exploration for Cold Start Problem

TL;DR

This paper proposes an item-centric approach to address the item cold-start problem in recommender systems, focusing on identifying the best users for new items to improve exploration and satisfaction.

Contribution

It introduces the concept of item-centric recommendations and a Bayesian control method to better target users for new items, enhancing cold-start performance.

Findings

Improved cold-start targeting efficacy

Enhanced user satisfaction with new content

Increased exploration efficiency

Abstract

Recommender systems face a critical challenge in the item cold-start problem, which limits content diversity and exacerbates popularity bias by struggling to recommend new items. While existing solutions often rely on auxiliary data, but this paper illuminates a distinct, yet equally pressing, issue stemming from the inherent user-centricity of many recommender systems. We argue that in environments with large and rapidly expanding item inventories, the traditional focus on finding the "best item for a user" can inadvertently obscure the ideal audience for nascent content. To counter this, we introduce the concept of item-centric recommendations, shifting the paradigm to identify the optimal users for new items. Our initial realization of this vision involves an item-centric control integrated into an exploration system. This control employs a Bayesian model with Beta distributions to assess candidate items based on a predicted balance between user satisfaction and the item's inherent quality. Empirical online evaluations reveal that this straightforward control markedly improves cold-start targeting efficacy, enhances user satisfaction with newly explored content, and significantly increases overall exploration efficiency.