Learning to evaluate and recommend query in restaurant search systems
Xian Chen (),
Hyoseop Shin () and
Hyang-won Lee ()
Additional contact information
Xian Chen: Konkuk University
Hyoseop Shin: Konkuk University
Hyang-won Lee: Konkuk University
Information Systems and e-Business Management, 2017, vol. 15, issue 1, No 3, 68 pages
Abstract Users tend to use their own terms to search items in structured search systems such as restaurant searches (e.g. Yelp), but due to users’ lack of understanding on internal vocabulary and structures, they often fail to adequately search, which leads to unsatisfying search results. In this case, search systems should assist users to use different terms for better search results. To address this issue, we develop a scheme to generate suggested queries, given a user query. We propose a scheme to evaluate queries (i.e. user queries and suggested queries) based on two measures: (1) if the query will return a sufficient number of search results, (2) if the query will return search results of high quality. Furthermore, we present a learning model to choose among alternative candidate queries against a user query. Then we provide learning to rank suggested queries and return to users. Our experiments show the proposed method provides feasible and scalable solution for query evaluation and recommendation of vertical search systems.
Keywords: Query recommender system; Quality of queries; Query suggestion; Learning to measure; Learning to rank (search for similar items in EconPapers)
References: View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s10257-016-0309-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:spr:infsem:v:15:y:2017:i:1:d:10.1007_s10257-016-0309-8
Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/10257
Access Statistics for this article
Information Systems and e-Business Management is currently edited by Jörg Becker and Michael J. Shaw
More articles in Information Systems and e-Business Management from Springer
Bibliographic data for series maintained by Sonal Shukla ().