A Ranking Method for Relaxed Queries in Book Search

by Momo Kyozuka, Yang Xu, Keishi Tajima


In this paper, we propose a ranking method for keyword-based book search systems. A user issues a query consisting of keywords describing the contents of the book, and the system returns a ranked list of candidate books. Because we do not have full text data of all the books, we use a database of brief descriptions of books in the market currently or in the past. When such brief descriptions are only available, some query keywords may not appear in the description of the book the user is looking for. To solve that problem, our method ranks books in two steps. We first generate relaxed queries by removing some keywords from the given original query, and rank them based on how likely the remaining keywords appear in the brief descriptions. We then retrieve matching books for each query, find words in the description that are the most similar to the removed keywords, and rank the books based on that similarity. By combining these two rankings, i.e., the ranking of relaxed queries, and the ranking of books matching with each query, we produce the final ranking. In this paper, we focus on the ranking method for the second step. Our experiment shows that our method is effective when the original query includes many keywords that do not appear in the description of the target book.

Full Text: pdf

Poster: pdf


query modification; query recommendation; query expansion
Published in Proc. of ACM/IEEE JCDL, pp.400-401, Urbana-Champaign, IL, 2019

tajima@i.kyoto-u.ac.jp / Fax: +81(Japan) 75-753-5978 / Office: Research Bldg. #7, room 404