4 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.IR2025★ 2 cited
A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems
Binglei Zhao, Houying Qi, Guang Xu +5
Large-scale recommendation systems often adopt cascading architecture consisting of retrieval, pre-ranking, ranking, and re-ranking stages. With strict latency requirements, pre-ra…
cs.IR2024
A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search
Huimu Wang, Mingming Li, Dadong Miao +5
Re-ranking is a process of rearranging ranking list to more effectively meet user demands by accounting for the interrelationships between items. Existing methods predominantly enh…
cs.IR2024★ 4 cited
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
Yuanbo Gao, Peng Lin, Dongyue Wang +4
Click-through rate (CTR) prediction is a core task in recommender systems. Existing methods (IDRec for short) rely on unique identities to represent distinct users and items that h…