Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context
Publication in refereed journal


摘要An important way to improve users' satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users' history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource-Delicious bookmark-to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data.
著者Bing LD, Lam W, Wong TL, Jameel S
期刊名稱ACM Transactions on Information Systems
詳細描述Article No. 6.
出版社Association for Computing Machinery (ACM)
關鍵詞Algorithms; Experimentation; graphical model; query log; social tagging; Web query reformulation
Web of Science 學科類別Computer Science; Computer Science, Information Systems

上次更新時間 2021-18-01 於 00:17