An incremental model on search engine query recommendation

Search engine query recommendation based on mining query logs has been considered as an important and useful method of facilitating users to retrieve information. However, the log data evolves quickly. Existing query recommendation approaches have to rebuild the models when new log data arrive. In this paper, we extend the query ranking model (QRM) proposed in our previous work (Wang et al., 2015) [1] to an adaptive model in which new coming log data is incrementally added, so that the recommendation model is kept up-to-date. The experimental results have demonstrated that the proposed incremental query ranking model (IQRM) is able to recommend queries more efficiently than re-building QRM on evolving log data without losing accuracy.