A Topicality Relevance-Aware Intent Model for Web Search

文献类型： 外文期刊

第一作者： Wang, Meng

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

作者机构：

关键词： Session search; search intent model; document re-ranking; topic relevance; transformer

期刊名称：IEEE ACCESS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN： 2169-3536

年卷期： 2023 年 11 卷

页码：

收录情况： SCI

摘要： To accurately understand user information needs and provide better search experiences, various methods have been proposed to model user search intent from their search logs. Most traditional methods based on query understanding only consider the similarity between the query and documents while ignoring the user's session interaction sequence. Some researchers also adopt neural network-based methods to model user search intent. However, most neural network-based models mainly focus on the user's session interaction sequence without considering the role of topic relevance in modeling user search intent. In this paper, we propose a novel topicality relevance-aware intent model (TRIM) for web search. TRIM consists of a topic relevance predictor and a user short-term intent predictor. The topic relevance predictor utilizes the BERT model to predict the topic relevance between the query and documents. The user short-term search intent predictor utilizes session context information to predict the user's short-term search intent. We further investigate several fusion strategies to integrate the topic relevance and user short-term search intent for user search intent prediction. We conduct our experiments on two public web search datasets named TianGong-QRef and TianGong-ST. The experiments show that TRIM outperforms all baselines in the document ranking task. On TianGong-QRef dataset, TRIM achieves a 15.19% increase over the best-performing baselines M-Match in terms of Mean Average Precision (MAP). On TianGong-ST dataset, TRIM achieves a 5.77% increase over the best-performing baselines CARS in terms of MAP. The experimental results indicate the effectiveness of topic relevance in user search intent modeling.

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