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Long short-term search session-based document re-ranking model

文献类型： 外文期刊

作者： Liu, Jianping 1 ; Wang, Meng 1 ; Wang, Jian 3 ; Wang, Yingfei 1 ; Chu, Xintao 1 ;

作者机构： 1.North Minzu Univ, Sch Comp Sci & Engn, Yinchuan 750021, Peoples R China

2.North Minzu Univ, Key Lab Images & Grap Intelligent Proc, State Ethn Affairs Commiss, Yinchuan 750021, Peoples R China

3.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

关键词： Document re-ranking; Long short-term session search; Memory network; BERT; User intent

期刊名称：KNOWLEDGE AND INFORMATION SYSTEMS （ 影响因子：3.1； 五年影响因子：2.8 ）

ISSN： 0219-1377

年卷期： 2025 年 67 卷 1 期

页码：

收录情况： SCI

摘要： Document re-ranking is a core task in session search. However, most existing methods only focus on the short-term session and ignore the long-term history sessions. This leads to inadequate understanding of the user's search intent, which affects the performance of model re-ranking. At the same time, these methods have weaker capability in understanding user queries. In this paper, we propose a long short-term search session-based re-ranking model (LSSRM). Firstly, we utilize the BERT model to predict the topic relevance between the query and candidate documents, in order to improve the model's understanding of user queries. Secondly, we initialize the reading vector with topic relevance and use the personalized memory encoder module to model the user's long-term search intent. Thirdly, we input the user's current session interaction sequence into Transformer to obtain the vector representation of the user's short-term search intent. Finally, the user's search intent and topical relevance information are hierarchically fused to obtain the final document ranking scores. Then re-rank the documents according to this score. We conduct extensive experiments on two real-world session datasets. The experimental results show that our method outperforms the baseline models for the document re-ranking task.

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