Skip to Main content Skip to Navigation
New interface
Conference papers

Location Based Recommender Systems (LBRS) – A Review

Abstract : Recommender system has a vital role in everyday life with newer advancement. Location based recommender system is the current trend involved in mobile devices by providing the user with their timely needs in an effective and efficient manner. The services provided by the location based recommender system are Geo-tagged data based services containing the Global Positioning System and sensors incorporated to accumulate user information. Bayesian network model is widely used in geo-tagged based services to provide solution to the cold start problem. Point Location based services considers user check-in and auxiliary information to provide recommendation. Regional based recommendation can be considered for improving accuracy in this Point location based service. Trajectory based services uses the travel paths of the user and finds place of interest along with the similar user behaviours. Context based information can be incorporated with these services to provide better recommendation. Thus this article provides an overview of the Geo-tagged media based services and Point Location based services and discusses about the possible research issues and future work that can be implemented.
Document type :
Conference papers
Complete list of metadata
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, November 18, 2021 - 2:20:55 PM
Last modification on : Thursday, November 18, 2021 - 2:32:06 PM
Long-term archiving on: : Saturday, February 19, 2022 - 7:12:33 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2023-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License



R. Sujithra @ Kanmani, B. Surendiran. Location Based Recommender Systems (LBRS) – A Review. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.320-328, ⟨10.1007/978-3-030-63467-4_27⟩. ⟨hal-03434790⟩



Record views