Learning to Measure Quality of Queries for Automatic Query Suggestion
Xian Chen, Hyoseop Shin · 2014
Users tend to use their own terms to search items in structured search systems such as restaurant searches (e.g. Yelp), but due to users ’ lack of understanding on internal vocabulary and structures, they often fail to adequately search, which leads to unsatisfying search results. In this case, search systems should assist users to use different terms for better search results. To address this issue, we develop a scheme to generate suggested queries, given a user query. At first in doing this, in this paper we propose a scheme to evaluate queries (i.e. user queries and/or suggested queries) based on two measures: 1) if the query will return a sufficient number of search results, 2) if the query will return search results of high quality. Furthermore, we present a learning model to choose among alternative candidate queries against a user query. Our experiments show the proposed method is feasible and scalable.