11 citations · 29 across the 9 of their papers we have counts for
9 papers
DEGR: Dual Exploration-Driven Generative Re-Ranking for Adaptive Cross-Request Context Bridging
Binglei Zhao, Xuanhua Yang, Xiwei Zhao +1
In industrial recommendation systems, the re-ranking stage balances business objectives and diversity for sequence-level optimization while modeling contextual information. However…
Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search
Enqiang Xu, Xinhui Li, Zhigong Zhou +6
In the rapidly evolving field of e-commerce, the effectiveness of search re-ranking models is crucial for enhancing user experience and driving conversion rates. Despite significan…
A Unified Search and Recommendation Framework Based on Multi-Scenario Learning for Ranking in E-commerce
Jinhan Liu, Qiyu Chen, Junjie Xu +3
Search and recommendation (S&R) are the two most important scenarios in e-commerce. The majority of users typically interact with products in S&R scenarios, indicating the need and…
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…
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce
Juan Gong, Zhenlin Chen, Chaoyi Ma +7
Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the use…
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
Jiongnan Liu, Zhicheng Dou, Guoyu Tang +1
Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize th…