DeepQFM: a deep learning based query facets mining method

• Published in: Information retrieval (Boston) 2023-12, Vol.26 (1-2), p.9, Article 9
• Main Authors: Deng, Zhirui, Dou, Zhicheng, Wen, Ji-Rong
• Format: Article
• Language: English
• Subjects: Computer Science; Data Mining and Knowledge Discovery; Data Structures and Information Theory; Deep learning; Documents; Information Storage and Retrieval; Mining; Natural Language Processing (NLP); Pattern Recognition; Queries; Search engines; Semantics; User satisfaction
• ISSN: 1386-4564; 1573-7659
• DOI: 10.1007/s10791-023-09427-0

Search results from the search engine may be not enough to satisfy users’ search intent when the issued query is broad or ambiguous. In such cases, presenting to the user query facets , which include common query reformulations, may help disambiguate the current query, save the effort of query reformulation, and improve the user’s search experience. Existing approaches for mining query facets are mainly based on rule-based statistical features, but ignore the deep semantic information which can measure the relationship between items in facets more precisely and find more potential facet items. In this paper, we introduce a deep learning model with contrastive learning for query facets mining—DeepQFM. We first extract items from search result documents, form lists containing items having a parallel structure, and weight these lists based on their importance. Then, we cluster the weighted lists based on their semantic distance. Finally, we train an item encoder with contrastive sampling and rank the facets and the facet items based on their semantic representation. Experimental results show that our deep query facets mining model outperforms the state-of-the-art approach QDMiner in almost all evaluation metrics, especially for the recall and rp-nDCG, suggesting that DeepQFM can effectively mine more facet items from search result documents.