978 citations · 1.3k across the 23 of their papers we have counts for
5 papers · 2 filters
pEBR: A Probabilistic Approach to Embedding Based Retrieval
Han Zhang, Yunjiang Jiang, Mingming Li +3
Embedding-based retrieval aims to learn a shared semantic representation space for both queries and items, enabling efficient and effective item retrieval through approximate neare…
Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval
Zhirui Kuai, Zuxu Chen, Huimu Wang +11
Generative retrieval (GR) has emerged as a transformative paradigm in search and recommender systems, leveraging numeric-based identifier representations to enhance efficiency and…
Generative Retrieval with Preference Optimization for E-commerce Search
Mingming Li, Huimu Wang, Zuxu Chen +5
Generative retrieval introduces a groundbreaking paradigm to document retrieval by directly generating the identifier of a pertinent document in response to a specific query. This…
Semantic-Enhanced Relational Metric Learning for Recommender Systems
Mingming Li, Fuqing Zhu, Feng Yuan +1
Recently, relational metric learning methods have been received great attention in recommendation community, which is inspired by the translation mechanism in knowledge graph. Diff…
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…