Ranking Methods for Query Relaxation in Book Search
by Momo Kyozuka, Keishi Tajima
Abstract
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.
query relaxation; query correction; query ranking; query suggestion; query recommendation
Published in Proc. of IEEE/WIC/ACM WI, pp.466-473, Santiago, Chile, 2018