{"ID":2895513,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09423","arxiv_id":"2507.09423","title":"Item-centric Exploration for Cold Start Problem","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.","short_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 i...","url_abs":"https://arxiv.org/abs/2507.09423","url_pdf":"https://arxiv.org/pdf/2507.09423v1","authors":"[\"Dong Wang\",\"Junyi Jiao\",\"Arnab Bhadury\",\"Yaping Zhang\",\"Mingyan Gao\",\"Onkar Dalal\"]","published":"2025-07-12T23:22:23Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"LoRA\"]","has_code":false}