Ranking Methods for Query Relaxation in Book Search

In this paper, we propose a method to support book search tasks where users issue a query describing the story in a book to a database storing brief descriptions of books. Such a query may include extraneous words that do not appear in the brief description of the book in the database. In addition, queries by users who only have vague memories of the stories may even include wrong keywords. In order to find books with such queries, we need a query relaxation scheme. In the scheme we propose in this paper, we classify words in a user query describing a book into four types based on their roles in the description, and for each type, we estimate the probability of their appearance in the description in the database. We estimate it based on statistics we obtained through an analysis of an archive of queries and answers in the past. We then generate relaxed queries by using every subset of the words in the user query, and rank the queries based on the expected ranking of the target book in their results. The expected ranking of the target book in a query result is estimated by using appearance probabilities of words in the query and the number of books matching the query. We conducted an experiment for comparing various ranking schemes by their MRR, and our ranking scheme that uses both the word appearance probabilities and the number of matching books showed a good performance.