{"ID":2828268,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16033","arxiv_id":"2512.16033","title":"On Recommending Category: A Cascading Approach","abstract":"Recommendation plays a key role in e-commerce, enhancing user experience and boosting commercial success. Existing works mainly focus on recommending a set of items, but online e-commerce platforms have recently begun to pay attention to exploring users' potential interests at the category level. Category-level recommendation allows e-commerce platforms to promote users' engagements by expanding their interests to different types of items. In addition, it complements item-level recommendations when the latter becomes extremely challenging for users with little-known information and past interactions. Furthermore, it facilitates item-level recommendations in existing works. The predicted category, which is called intention in those works, aids the exploration of item-level preference. However, such category-level preference prediction has mostly been accomplished through applying item-level models. Some key differences between item-level recommendations and category-level recommendations are ignored in such a simplistic adaptation. In this paper, we propose a cascading category recommender (CCRec) model with a variational autoencoder (VAE) to encode item-level information to perform category-level recommendations. Experiments show the advantages of this model over methods designed for item-level recommendations.","short_abstract":"Recommendation plays a key role in e-commerce, enhancing user experience and boosting commercial success. Existing works mainly focus on recommending a set of items, but online e-commerce platforms have recently begun to pay attention to exploring users' potential interests at the category level. Category-level recomme...","url_abs":"https://arxiv.org/abs/2512.16033","url_pdf":"https://arxiv.org/pdf/2512.16033v1","authors":"[\"Qihao Wang\",\"Pritom Saha Akash\",\"Varvara Kollia\",\"Kevin Chen-Chuan Chang\",\"Biwei Jiang\",\"Vadim Von Brzeski\"]","published":"2025-12-17T23:32:33Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"LoRA\",\"Variational Autoencoder\"]","has_code":false}