NDLI: Nontrivial landmark recommendation using geotagged photos

Content Provider	ACM Digital Library
Author	Serdyukov, Pavel Shi, Yue Larson, Martha Hanjalic, Alan
Copyright Year	2013
Abstract	Online photo-sharing sites provide a wealth of information about user behavior and their potential is increasing as it becomes ever-more common for images to be associated with location information in the form of geotags. In this article, we propose a novel approach that exploits geotagged images from an online community for the purpose of personalized landmark recommendation. Under our formulation of the task, recommended landmarks should be relevant to user interests and additionally they should constitute nontrivial recommendations. In other words, recommendations of landmarks that are highly popular and frequently visited and can be easily discovered through other information sources such as travel guides should be avoided in favor of recommendations that relate to users' personal interests. We propose a collaborative filtering approach to the personalized landmark recommendation task within a matrix factorization framework. Our approach, WMF-CR, combines weighted matrix factorization and category-based regularization. The integrated weights emphasize the contribution of nontrivial landmarks in order to focus the recommendation model specifically on the generation of nontrivial recommendations. They support the judicious elimination of trivial landmarks from consideration without also discarding information valuable for recommendation. Category-based regularization addresses the sparse data problem, which is arguably even greater in the case of our landmark recommendation task than in other recommendation scenarios due to the limited amount of travel experience recorded in the online image set of any given user. We use category information extracted from Wikipedia in order to provide the system with a method to generalize the semantics of landmarks and allow the model to relate them not only on the basis of identity, but also on the basis of topical commonality. The proposed approach is computational scalable, that is, its complexity is linear with the number of observed preferences in the user-landmark preference matrix and the number of nonzero similarities in the category-based landmark similarity matrix. We evaluate the approach on a large collection of geotagged photos gathered from Flickr. Our experimental results demonstrate that WMF-CR outperforms several state-of-the-art baseline approaches in recommending nontrivial landmarks. Additionally, they demonstrate that the approach is well suited for addressing data sparseness and provides particular performance improvement in the case of users who have limited travel experience, that is, have visited only few cities or few landmarks.
Starting Page	1
Ending Page	27
Page Count	27
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2483669.2483680
Volume Number	4
Issue Number	3
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-07-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Collaborative filtering Geotag Location-based recommendation Nontrivial recommendation Social media application
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

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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
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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