NDLI: Learning to evaluate and recommend query in restaurant search systems

Please wait, while we are loading the content...

The effect of price promotions on consumer shopping behavior across online and offline channels: differences between frequent and non-frequent shoppers

Factors influencing adoption of e-payment systems: an empirical study on Iranian customers

Gender classification of microblog text based on authorial style

StockProF: a stock profiling framework using data mining approaches

Explaining organizational susceptibility to coercive pressure: results from a field experiment on e-invoicing IOIS adoption

Exploring gender differences in acceptance of mobile computing devices among college students

Information Systems and e-Business Management : Volume 14

Information Systems and e-Business Management : Volume 13

Information Systems and e-Business Management : Volume 12

Information Systems and e-Business Management : Volume 11

Information Systems and e-Business Management : Volume 10

Information Systems and e-Business Management : Volume 9

Information Systems and e-Business Management : Volume 8

Information Systems and e-Business Management : Volume 7

Information Systems and e-Business Management : Volume 6

Information Systems and e-Business Management : Volume 5

Information Systems and e-Business Management : Volume 4

Information Systems and e-Business Management : Volume 3

Information Systems and e-Business Management : Volume 2

Information Systems and e-Business Management : Volume 1

Learning to evaluate and recommend query in restaurant search systems

Content Provider	Springer Nature Link
Author	Chen, Xian Shin, Hyoseop Lee, Hyang won
Copyright Year	2016
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.
Starting Page	51
Ending Page	68
Page Count	18
File Format	PDF
ISSN	16179846
Journal	Information Systems and e-Business Management
Volume Number	15
Issue Number	1
e-ISSN	16179854
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-05-02
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Query recommender system Quality of queries Query suggestion Learning to measure Learning to rank IT in Business Information Systems Applications (incl.Internet) Management
Content Type	Text
Resource Type	Article
Subject	Information Systems

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

An effective suggestion method for keyword search of databases

Assisting web search using query suggestion based on word similarity measure and query modification patterns

IT-enabled business process management

Efficient range query processing in metric spaces over highly distributed data

Indexing views to route queries in a PDMS

Query-Free News Search

Semantics and evaluation of top-k queries in probabilistic databases

Distributed top-k aggregation queries at large

A picture-based approach to recommender systems

Learning to evaluate and recommend query in restaurant search systems

Similar Documents

An effective suggestion method for keyword search of databases

Assisting web search using query suggestion based on word similarity measure and query modification patterns

IT-enabled business process management

Efficient range query processing in metric spaces over highly distributed data

Indexing views to route queries in a PDMS

Query-Free News Search

Semantics and evaluation of top-k queries in probabilistic databases

Distributed top-k aggregation queries at large

A picture-based approach to recommender systems

Learning to evaluate and recommend query in restaurant search systems