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. |
| 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. |
| 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.
Publication details
- Title: Recommender Systems for Location‑based Social Networks
- Publisher: Springer New York
- Series: SpringerBriefs in Electrical and Computer Engineering
- eBook ISBN: 978-1-4939-0286-6
- Published: 08 February 2014
- Pages: 108
- Authors: Panagiotis Symeonidis, Dimitrios Ntempos, Yannis Manolopoulos