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

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Abstract

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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Availability of data and materials

The datasets we use are all public.

Notes

  1. http://www.thuir.cn/tiangong-qref/.

  2. https://www.kaggle.com/c/yandex-personalized-web-search-challenge.

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Acknowledgements

We thank Tsinghua University for providing us with the TianGong-QRef dataset.

Funding

“This work was supported by the Key scientific research projects of North Minzu University “Study on grape cluster detection and leaf disease identification based on semi- supervised transfer learning” under Grant 2023ZRLG12; in part by the Ministry of Education Industry-University Cooperation Synergistic Education Program titled “Teaching Content Reform of ‘Intelligent Recommendation System’ Based on Cloud Big Data Platform” under Grant 220802539112039; in part by the Starting Project of Scientific Research in the North Minzu University titled “Research of Information Retrieval Model Based on the Decision Process” under Grant 2020KYQD37.”

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Author notes

  1. Jian Wang, Yingfei Wang and Xintao Chu have contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China

    Jianping Liu, Meng Wang, Yingfei Wang & Xintao Chu

  2. The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China

    Jianping Liu

  3. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Jian Wang

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Contributions

J.L. and M.W. wrote the main manuscript text, J.W. translated the full text, Y.W. prepared figures, and X.C. prepared tables. All authors reviewed the manuscript.

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Correspondence to Meng Wang.

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Liu, J., Wang, M., Wang, J. et al. Long short-term search session-based document re-ranking model. Knowl Inf Syst 67, 223–245 (2025). https://doi.org/10.1007/s10115-024-02205-4

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