A Community Based Location Recommendation System For Location Based Social Networks
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Recommender Systems for Location-based Social Networks
Author: Panagiotis Symeonidis
language: en
Publisher: Springer Science & Business Media
Release Date: 2014-02-08
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.
A Community-based Location Recommendation System for Location-based Social Networks
"In recent years, location-based social networks (LBSNs) has become more and more popular. As one of the key service in LBSNs, the location recommendation system has drawn much of attention from both industry and academia. According to existing work, link analysis-based methods have been proved to be effective inlocation recommendations for LBSNs. However, most of link analysis-based methods either overlook or overemphasize users' preferences. Recommendation systems that overlook users' preferences can only provide generic recommendation, while systems that overemphasize users' preference cannot recommend local popular locations that do not fit users' historical preferences. To address these issues, in this thesis, I propose a community-based location recommendation system, which takes both users' preferences and locations' popularity into account. Our system groups locations within the user-specified region into communities. Each community represents one location category and will generate a certain number of recommendations. More specifically, communities that represent user-favored categories and communities that contain large number of popular locations have higher priorities to recommend more locations. Besides, the number of recommendations of each community is dynamically calculated for different users at different regions. Thus, our system can cover both user-favored and local popular locations in its recommendations. In the evaluation, we acquire data from Foursquare, which contains 398,819 tips generated by 49,027 users who has visited the New York City. Our recommendation system outperforms the baseline approach with the precision and recall of 52.13%. and 80.01% respectively. The experimental result demonstrates that our system can provide more accurate recommendations with acceptable computation time for various types of users and solve the new-user problem as well." --
Cross-Cultural Design
Author: Pei-Luen Patrick Rau
language: en
Publisher: Springer Nature
Release Date: 2023-07-08
This three-volume set of CCD 2023, constitutes the refereed proceedings of the 25th International Conference on Cross-Cultural Design, CCD 2023, held as Part of the 24th International Conference, HCI International 2023, which took place in July 2023 in Copenhagen, Denmark. The total of 1578 papers and 396 posters included in the HCII 2023 proceedings volumes was carefully reviewed and selected from 7472 submissions. The papers of CCD 2023, Part I address topics related to service and product design for cultural innovation, design for social change and development, sustainable design methods and practices, and cross-cultural perspectives on design and consumer behavior.