Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
More than relevance: high utility query recommendation by mining users' search behaviors
Proceedings of the 21st ACM international conference on Information and knowledge management
QUBiC: An adaptive approach to query-based recommendation
Journal of Intelligent Information Systems
Intent models for contextualising and diversifying query suggestions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
Query recommendation has been widely used in modern search engines. Recently, several context-aware methods have been proposed to improve the accuracy of recommendation by mining query sequence patterns from query sessions. However, the existing methods usually do not address the ambiguity of queries explicitly and often suffer from the sparsity of the training data. In this paper, we propose a novel context-aware query recommendation approach by modeling the high-order relation between queries and clicks in query log, which captures users' latent search intents. Empirical experiment results demonstrate that our approach outperforms the baseline methods in providing high quality recommendations for ambiguous queries.