Entity-Based Query Recommendation for Long-Tail Queries - Laboratoire Interdisciplinaire des Sciences du Numérique Access content directly
Journal Articles ACM Transactions on Knowledge Discovery from Data (TKDD) Year : 2018

Entity-Based Query Recommendation for Long-Tail Queries


Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs ), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.
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Dates and versions

hal-03203582 , version 1 (20-04-2021)



Zhipeng Huang, Bogdan Cautis, Reynold Cheng, Yudian Zheng, Nikos Mamoulis, et al.. Entity-Based Query Recommendation for Long-Tail Queries. ACM Transactions on Knowledge Discovery from Data (TKDD), 2018, 12 (6), pp.1-24. ⟨10.1145/3233186⟩. ⟨hal-03203582⟩
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