| Autor: |
Meng Wang, Jianping Liu, Jian Wang, Yingfei Wang, Xintao Chu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 11, Pp 65739-65748 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3289820 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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