An efficient hybrid query recommendation using shingling and hashing techniques

Abstract

Search engines recommend queries to improve the satisfaction level of users by shortening their search task. A proper solution for query recommendation is to analyze the users’ behaviors and mimic the query transition patterns adopted by different users who are succeeded in finding their needed information. In this paper, we propose a novel three-layer query recommendation method which is benefited from a query community graph in the first layer. This graph is generated through clustering similar queries which tend to convey the same meaning. To reduce the overhead of clustering while preserving its performance, we utilize locality-sensitive hashing of k-shingles to represent queries in a space with smaller and fixed dimensions. The second layer is enriched by a query-flow graph which models the transitional patterns made by users inside sessions. The hybrid graph, created by consolidating the query community and query-flow graphs, takes into account the lexical similarity as well as the reformulation diversity to suggest queries. The results of our experiments on data logs of two real search engines show that the proposed method outperforms some well-known algorithms by at least 14% with respect to precision and P@10 parameters.