Improving Session Search by Modeling Multi-Granularity Historical Query Change

被引:10
作者
Zuo, Xiaochen
Dou, Zhicheng [1 ]
Wen, Ji-Rong
机构
[1] Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing, Peoples R China
来源
WSDM'22: PROCEEDINGS OF THE FIFTEENTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING | 2022年
基金
中国国家自然科学基金;
关键词
Session Search; Query Change; Document Ranking; REFORMULATION;
D O I
10.1145/3488560.3498415
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.
引用
收藏
页码:1534 / 1542
页数:9
相关论文
共 34 条
  • [1] Agichtein E, 2012, SIGIR 2012: PROCEEDINGS OF THE 35TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P315, DOI 10.1145/2348283.2348328
  • [2] Context Attentive Document Ranking and Query Suggestion
    Ahmad, Wasi Uddin
    Chang, Kai-Wei
    Wang, Hongning
    [J]. PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19), 2019, : 385 - 394
  • [3] Ahmad Wasi Uddin, 2018, 6 INT C LEARNING REP
  • [4] [Anonymous], 2014, Advances in Neural Information Processing Systems
  • [5] Cao H., 2009, P 18 INT C WORLD WID, P191, DOI [10.1145/1526709.1526736, DOI 10.1145/1526709.1526736]
  • [6] TianGong-ST: A New Dataset with Large-scale Refined Real-world Web Search Sessions
    Chen, Jia
    Mao, Jiaxin
    Liu, Yiqun
    Zhang, Min
    Ma, Shaoping
    [J]. PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19), 2019, : 2485 - 2488
  • [7] Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
    Dehghani, Mostafa
    Rothe, Sascha
    Alfonseca, Enrique
    Fleury, Pascal
    [J]. CIKM'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2017, : 1747 - 1756
  • [8] Graves A, 2012, STUD COMPUT INTELL, V385, P1, DOI [10.1007/978-3-642-24797-2, 10.1162/neco.1997.9.1.1]
  • [9] Guan DY, 2013, SIGIR'13: THE PROCEEDINGS OF THE 36TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH & DEVELOPMENT IN INFORMATION RETRIEVAL, P453
  • [10] Halder Kishaloy., 2020, ABS200100861 CORR