Assisting Web Search Users by Destination Reachability

摘要

Search engine users are increasingly performing complex tasks based on the simple keyword-in document-out paradigm. To assist users in accomplishing their tasks effectively, search engines provide query recommendations based on the user's current query. These are suggestions for follow-up queries given the user-provided query. A large number of techniques have been proposed in the past on mining such query recommendations which include past user sessions (e.g., sequence of queries within a specified window of time) to identify most frequently occurring pairs, using click-through graphs (e.g.. a bipartite graph of queries and the urls on which users clicked) and rank these suggestions using some form of frequency counts from the past query logs. Given the limited number of queries that are offered (typically 5) it-is important to effectively rank them. In this paper, we present a novel approach to ranking query recommendations which not only consider relevance to the original query but also take into account efficiency of a query at accomplishing a user search task at hand. We formalize the notion of query efficiency and show how our objective function effectively captures this as determined by a human study and eliminates biases introduced by click-through based metrics. To compute this objective function, we present a pseudo-supervised learning technique where no explicit human experts are required to label samples. In addition, our techniques effectively characterize preferred url destinations and project each query into a higher dimension space where each sub-spaces represents user intent using these characteristics. Finally, we present an extensive evaluation of our proposed methods against production systems and show our method to increase task completion efficiency by 15%.