KB-Enabled Query Recommendation for Long-Tail Queries

Zhipeng Huang (1) , Bogdan Cautis (2) , Reynold Cheng (1) , Yudian Zheng (1)

1 HKU - The University of Hong Kong
2 LaHDAK - LRI - Données et Connaissances Massives et Hétérogènes (LRI)

In recent years, query recommendation algorithms have been designed to provide related queries for search engine users. Most of these solutions, which perform extensive analysis of users' search history (or query logs), are largely insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study a new solution, which makes use of a knowledge base (or KB), such as YAGO and Freebase. A KB is a rich information source that describes how real-world entities are connected. We extract entities from a query, and use these entities to explore new ones in the KB. Those discovered entities are then used to suggest new queries to the user. As shown in our experiments, our approach provides better recommendation results for long-tail queries than existing solutions.

Domaines: Informatique [cs]; Base de données [cs.DB]; Recherche d'information [cs.IR]

Zhipeng Huang, Bogdan Cautis, Reynold Cheng, Yudian Zheng. KB-Enabled Query Recommendation for Long-Tail Queries. CIKM 2016 - the 25th ACM International Conference on Information and Knowledge Management, Oct 2016, Indianapolis, United States. ⟨10.1145/2983323.2983650⟩. ⟨hal-01687905⟩