Item-centric Exploration for Cold Start Problem

• URL: http://arxiv.org/abs/2507.09423v1
• Date: Sat, 12 Jul 2025 23:22:23 GMT
• Title: Item-centric Exploration for Cold Start Problem
• Authors: Dong Wang, Junyi Jiao, Arnab Bhadury, Yaping Zhang, Mingyan Gao, Onkar Dalal,
• Abstract summary: We argue that the traditional focus on finding the "best item for a user" can inadvertently obscure the ideal audience for nascent content.<n>We introduce the concept of item-centric recommendations, shifting the paradigm to identify the optimal users for new items.
• Score: 7.132196640884619
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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