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Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems

Published: 23 January 2018 Publication History

Abstract

Because of the widespread adoption of GPS-enabled devices, such as smartphones and GPS navigation devices, more and more location information is being collected and available. Compared with traditional ones (e.g., Amazon, Taobao, and Dangdang), recommender systems built on location-based social networks (LBSNs) have received much attention. The former mine users’ preferences through the relationship between users and items, e.g., online commodity, movies and music. The latter add location information as a new dimension to the former, hence resulting in a three-dimensional relationship among users, locations, and activities. In this article, we summarize LBSN recommender systems from the perspective of such a relationship. User, activity, and location are called objects, and recommender objectives are formed and achieved by mining and using such 3D relationships. From the perspective of the 3D relationship among these objects, we summarize the state-of-the-art of LBSN recommender systems to fulfill the related objectives. We finally indicate some future research directions in this area.

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  1. Objectives and State-of-the-Art of Location-Based Social Network Recommender Systems

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      Published In

      cover image ACM Computing Surveys
      ACM Computing Surveys  Volume 51, Issue 1
      January 2019
      743 pages
      ISSN:0360-0300
      EISSN:1557-7341
      DOI:10.1145/3177787
      • Editor:
      • Sartaj Sahni
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 23 January 2018
      Accepted: 01 October 2017
      Revised: 01 June 2017
      Received: 01 December 2015
      Published in CSUR Volume 51, Issue 1

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      Author Tags

      1. Location-based social networks
      2. recommender objectives

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      • Survey
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      • Refereed

      Funding Sources

      • National Natural Science Funds of P.R. China

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      • (2025)An introduction to collaborative filtering through the lens of the Netflix PrizeKnowledge and Information Systems10.1007/s10115-024-02315-zOnline publication date: 10-Jan-2025
      • (2024)Intelligent recommendation algorithm for social networks based on improving a generalized regression neural networkElectronic Research Archive10.3934/era.202419732:7(4378-4397)Online publication date: 2024
      • (2024)Towards integration of artificial intelligence into medical devices as a real-time recommender system for personalised healthcare: State-of-the-art and future prospectsHealth Sciences Review10.1016/j.hsr.2024.10015010(100150)Online publication date: Mar-2024
      • (2024)Recent trends in recommender systems: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00349-113:4Online publication date: 10-Oct-2024
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      • (2023)A Context- and Trajectory-Based Destination Prediction of Public Transportation UsersIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2021.313277215:1(300-317)Online publication date: Jan-2023
      • (2023)Unbox the Black-Box: Predict and Interpret YouTube Viewership Using Deep LearningJournal of Management Information Systems10.1080/07421222.2023.219678040:2(541-579)Online publication date: 17-Jun-2023
      • (2023)Enhanced personalized recommendation system for machine learning public datasets: generalized modeling, simulation, significant results and analysisInternational Journal of Information Technology10.1007/s41870-023-01165-215:3(1583-1595)Online publication date: 13-Feb-2023
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