Suggestion set utility maximization using session logs.

Assistance technology is undoubtedly one of the important elements in the commercial search engines, and routing the user towards the right direction throughout the search sessions is of great importance for providing a good search experience. Most search assistance methods in the literature that involve query generation, query expansion and other techniques consider each suggestion candidate individually, which implies an independence assumption. We challenge this independence assumption and give a method to maximize the utility of a given set of suggestions. For this, we will define a measure of conditional utility for query pairs using query-URL bipartite graphs based on the session logs (clicked and viewed URLs). Afterwards, we remove the redundant queries from the suggestion set using a greedy algorithm to be able to replace them with more useful ones. Both offline (based on user studies and session log analysis) and online (based on millions of user interactions) evaluations show that modeling the conditional utility and maximizing the utility of the set of queries (by eliminating redundant ones) significantly increases the effectiveness of the search assistance both for the presubmit and postsubmit modes.