Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Search the web x.0: mining and recommending web-mediated processes
Proceedings of the third ACM conference on Recommender systems
Automobile, car and BMW: horizontal and hierarchical approach in social tagging systems
Proceedings of the 2nd ACM workshop on Social web search and mining
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
First query term extraction from current webpage for mobile applications
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Sparse hidden-dynamics conditional random fields for user intent understanding
Proceedings of the 20th international conference on World wide web
Context-sensitive query auto-completion
Proceedings of the 20th international conference on World wide web
Folksonomy query suggestion via users' search intent prediction
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
Structured query suggestion for specialization and parallel movement: effect on search behaviors
Proceedings of the 21st international conference on World Wide Web
HMM-CARe: Hidden Markov Models for context-aware tag recommendation in folksonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Context-Aware personalized search based on user and resource profiles in folksonomies
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Context-aware document recommendation by mining sequential access data
Proceedings of the 1st International Workshop on Context Discovery and Data Mining
Efficient error-tolerant query autocompletion
Proceedings of the VLDB Endowment
When do people use query suggestion? A query suggestion log analysis
Information Retrieval
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Web query recommendation has long been considered a key feature of search engines. Building a good Web query recommendation system, however, is very difficult due to the fundamental challenge of predicting users' search intent, especially given the limited user context information. In this paper, we propose a novel "sequential query prediction" approach that tries to grasp a user's search intent based on his/her past query sequence and its resemblance to historical query sequence models mined from massive search engine logs. Different query sequence models were examined, including the naive variable length N-gram model, Variable Memory Markov (VMM) model, and our proposed Mixture Variable Memory Markov (MVMM) model. Extensive experiments were conducted to benchmark our sequence prediction algorithms against two conventional pairwise approaches on large-scale search logs extracted from a commercial search engine. Results show that the sequence-wise approaches significantly outperform the conventional pair-wise ones in terms of prediction accuracy. In particular, our MVMM approach, consistently leads the pack, making it an effective and practical approach towards Web query recommendation.