Suggesting Topic-Based Query Terms as You Type

— 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 ...

• Type: Journal
• Year: 2010
• Where: APWEB
• Authors: Ju Fan, Hao Wu, Guoliang Li, Lizhu Zhou
• Keywords: APWEB 2010, Internet Technology, Query Term Suggestion, Relevant Documents, Term Suggestion