Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
COMMA: A Result-Oriented Composite Autocompletion Method for E-marketplaces
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Learning to personalize query auto-completion
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Behavioral dynamics on the web: Learning, modeling, and prediction
ACM Transactions on Information Systems (TOIS)
Relational term-suggestion graphs incorporating multipartite concept and expertise networks
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Web Intelligence and Agent Systems
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Query term suggestion that interactively expands the queries is an indispensable technique to help users formulate high-quality queries and has attracted much attention in the community of web search. Existing methods usually suggest terms based on statistics in documents as well as query logs and external dictionaries, and they neglect the fact that the topic information is very crucial because it helps retrieve topically relevant documents. To give users gratification, we propose a novel term suggestion method: as the user types in queries letter by letter, we suggest the terms that are topically coherent with the query and could retrieve relevant documents instantly. For effectively suggesting highly relevant terms, we propose a generative model by incorporating the topical coherence of terms. The model learns the topics from the underlying documents based on Latent Dirichlet Allocation (LDA). For achieving the goal of instant query suggestion, we use a trie structure to index and access terms. We devise an efficient top-k algorithm to suggest terms as users type in queries. Experimental results show that our approach not only improves the effectiveness of term suggestion, but also achieves better efficiency and scalability.