Recommending Search Queries in Documents Using Inter N-Gram Similarities

Reading a document can often trigger a need for additional information. For example, a reader of a news article might be interested in information about the persons and events mentioned in the article. Accordingly, there is a line of work on recommending search-engine queries given a document read by a user. Often, the recommended queries are selected from a query log independently of each other, and are presented to the user without any context. We address a novel query recommendation task where the recommended queries must be n-grams (sequences of consecutive terms) in the document. Furthermore, inspired by work on using inter-document similarities for document retrieval, we explore the merits of using inter n-gram similarities for query recommendation. Specifically, we use a supervised approach to learn an inter n-gram similarity measure where the goal is that n-grams that are likely to serve as queries will be deemed more similar to each other than to other n-grams. We use the similarity measure in a wide variety of query recommendation approaches which we devise as adaptations of ad hoc document retrieval techniques. Empirical evaluation performed using data gathered from Yahoo!'s search engine logs attests to the effectiveness of the resultant recommendation methods.

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