Recommender Systems for Location-based Social Networks

  • Panagiotis Symeonidis
  • Dimitrios Ntempos
  • Yannis Manolopoulos

Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-v
  2. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
    Pages 1-4
  3. Basic Definitions and Concepts

    1. Front Matter
      Pages 5-5
    2. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 7-20
    3. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 21-34
    4. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 35-48
  4. Recommendation Algorithms in LBSNs

    1. Front Matter
      Pages 49-49
    2. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 51-66
    3. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 67-79
    4. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 81-86
  5. Implementing a Real-World LBSN

    1. Front Matter
      Pages 87-87
    2. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 89-105
    3. Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos
      Pages 107-108

About this book

Introduction

Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs.

The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of these recommender systems. Part 3 provides a step-by-step case study on the technical aspects of deploying and evaluating a real-world LBSN, which provides location, activity and friend recommendations. The material covered in the book is intended for graduate students, teachers, researchers, and practitioners in the areas of web data mining, information retrieval, and machine learning.

Keywords

Algorithms information filtering location-based social networks performance real-world case study recommender systems social and media web web 2.0

Authors and affiliations

  • Panagiotis Symeonidis
    • 1
  • Dimitrios Ntempos
    • 2
  • Yannis Manolopoulos
    • 3
  1. 1.Department of Informatics Data Engineering LaboratoryAristotle University of ThessalonikiStavroupoliGreece
  2. 2.Kiwe DevelopmentKalamariaGreece
  3. 3.Department of Informatics Data Engineering LabAristotle University of ThessalonikiStavroupoliGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-0286-6
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Engineering
  • Print ISBN 978-1-4939-0285-9
  • Online ISBN 978-1-4939-0286-6
  • Series Print ISSN 2191-8112
  • Series Online ISSN 2191-8120
  • About this book