Recommending high-utility search engine queries via a query-recommending model

Query recommendation technology is of great importance for search engines, because it can assist users to find the information they require. Many query recommendation algorithms have been proposed, but they all aim to recommend similar queries and cannot guarantee the usefulness of the recommended queries. In this paper, we argue that it is more important to recommend high-utility queries, i.e., queries that would induce users to search for more useful information. For this purpose, we propose a query-recommending model to rank candidate queries according to their utilities and to recommend those that are useful to users. The query-recommending model ranks a candidate query by assessing the joint probability that the query is selected by the user, that the obtained search results are subsequently clicked by the user, and that the clicked search results ultimately satisfy the user׳s information need. Three utilities were defined to solve the model: query-level utility, representing the attractiveness of a query to the user; perceived utility, measuring the user׳s probability of clicking on the search results; and posterior utility, measuring the useful information obtained by the user from the clicked search results. The methods that were used to compute these three utilities from the query log data are presented. The experimental results that were obtained by using real query log data demonstrated that the proposed query-recommending model outperformed six other baseline methods in generating more useful recommendations.