document: Optimal rare query suggestion with implicit user feedback Query suggestion has been an effective approach to help users narrow down to the information they need . However , most of existing studies focused on only popular\/head queries . Since rare queries possess much less information ( e.g. , clicks ) than popular queries in the query logs , it is much more difficult to efficiently suggest relevant queries to a rare query . In this paper , we propose an optimal rare query suggestion framework by leveraging implicit feedbacks from users in the query logs . Our model resembles the principle of pseudo-relevance feedback which assumes that top-returned results by search engines are relevant . However , we argue that the clicked URLs and skipped URLs contain different levels of information and thus should be treated differently . Hence , our framework optimally combines both the click and skip information from users and uses a random walk model to optimize the query correlation . Our model specifically optimizes two parameters : ( 1 ) the restarting ( jumping ) rate of random walk , and ( 2 ) the combination ratio of click and skip information . Unlike the Rocchio algorithm , our learning process does not involve the content of the URLs but simply leverages the click and skip counts in the query-URL bipartite graphs . Consequently , our model is capable of scaling up to the need of commercial search engines . Experimental results on one-month query logs from a large commercial search engine with over 40 million rare queries demonstrate the superiority of our framework , with statistical significance , over the traditional random walk models and pseudo-relevance feedback models .
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