Recommendation In Location Based Social Networks
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| Author | : Shenglin Zhao |
| Publisher | : Springer |
| Total Pages | : 110 |
| Release | : 2018-07-13 |
| Genre | : Computers |
| ISBN | : 9811313490 |
This book systematically introduces Point-of-interest (POI) recommendations in Location-based Social Networks (LBSNs). Starting with a review of the advances in this area, the book then analyzes user mobility in LBSNs from geographical and temporal perspectives. Further, it demonstrates how to build a state-of-the-art POI recommendation system by incorporating the user behavior analysis. Lastly, the book discusses future research directions in this area. This book is intended for professionals involved in POI recommendation and graduate students working on problems related to location-based services. It is assumed that readers have a basic knowledge of mathematics, as well as some background in recommendation systems.
| Author | : Panagiotis Symeonidis |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 109 |
| Release | : 2014-02-08 |
| Genre | : Computers |
| ISBN | : 1493902865 |
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.
| Author | : Bo Hu |
| Publisher | : |
| Total Pages | : 110 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Recommender systems have become popular tools to select relevant personalized information for users. With the rapid growth of mobile network users, the way users consume Web 2.0 is changing substantially. Mobile networks enable users to post personal status on online social media services from anywhere and at anytime. However, as the volume of user activities is growing rapidly, it is getting impossible that for users to read all posts or blogs to catch up with the trends. Similarly, it is hard for producers and manufactures to monitor consumers and figure out their tastes. These needs inspired the emergence of a new line of research, recommendation in location-based social networks, i.e., building recommender systems to discover and predict the behavior of users and their engagement with location-based social networks. Extracted users' interests and their spatio-temporal patterns clearly provide more detailed information for producers to make decisions to supply their consumers. In this thesis, we address the problem of recommendation in location-based social networks and seek novel methods to improve limitations of existing techniques. We first propose a spatial topic model for top-k POI recommendation problem, and the proposed model discovers users' topic and geographical distributions from user check-ins with posts and location coordinates. Then we focus on mining spatio-temporal patterns of user check-ins and propose a spatio-temporal topic model to identify temporal activity patterns of different topics and POIs. In our next work, we argue that all existing social network-based POI recommendation models cannot capture the nature of location-based social network. Hence, we propose a social topic model to effectively exploit a location-based social network. Finally, we address the problem of determining the optimal location for a new store by considering it as a recommendation problem, i.e., recommending locations to a new store. Latent factor models are proposed and proved to perform better than existing state-of-the-art methods.
| Author | : Shashi Shekhar |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 1392 |
| Release | : 2007-12-12 |
| Genre | : Computers |
| ISBN | : 038730858X |
The Encyclopedia of GIS provides a comprehensive and authoritative guide, contributed by experts and peer-reviewed for accuracy, and alphabetically arranged for convenient access. The entries explain key software and processes used by geographers and computational scientists. Major overviews are provided for nearly 200 topics: Geoinformatics, Spatial Cognition, and Location-Based Services and more. Shorter entries define specific terms and concepts. The reference will be published as a print volume with abundant black and white art, and simultaneously as an XML online reference with hyperlinked citations, cross-references, four-color art, links to web-based maps, and other interactive features.
| Author | : Rifeng Ding |
| Publisher | : |
| Total Pages | : |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
"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." --
| Author | : Huiji Gao |
| Publisher | : Springer Nature |
| Total Pages | : 99 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031019083 |
In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to "check in" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely location-based social networks (LBSNs). Compared to traditional GPS data, location-based social networks data contains unique properties with abundant heterogeneous information to reveal human mobility, i.e., "when and where a user (who) has been to for what," corresponding to an unprecedented opportunity to better understand human mobility from spatial, temporal, social, and content aspects. The mining and understanding of human mobility can further lead to effective approaches to improve current location-based services from mobile marketing to recommender systems, providing users more convenient life experience than before. This book takes a data mining perspective to offer an overview of studying human mobility in location-based social networks and illuminate a wide range of related computational tasks. It introduces basic concepts, elaborates associated challenges, reviews state-of-the-art algorithms with illustrative examples and real-world LBSN datasets, and discusses effective evaluation methods in mining human mobility. In particular, we illustrate unique characteristics and research opportunities of LBSN data, present representative tasks of mining human mobility on location-based social networks, including capturing user mobility patterns to understand when and where a user commonly goes (location prediction), and exploiting user preferences and location profiles to investigate where and when a user wants to explore (location recommendation), along with studying a user's check-in activity in terms of why a user goes to a certain location.
| Author | : Yu Zheng |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 328 |
| Release | : 2011-10-02 |
| Genre | : Computers |
| ISBN | : 1461416299 |
Spatial trajectories have been bringing the unprecedented wealth to a variety of research communities. A spatial trajectory records the paths of a variety of moving objects, such as people who log their travel routes with GPS trajectories. The field of moving objects related research has become extremely active within the last few years, especially with all major database and data mining conferences and journals. Computing with Spatial Trajectories introduces the algorithms, technologies, and systems used to process, manage and understand existing spatial trajectories for different applications. This book also presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks. Each chapter provides readers with a tutorial-style introduction to one important aspect of location trajectory computing, case studies and many valuable references to other relevant research work. Computing with Spatial Trajectories is designed as a reference or secondary text book for advanced-level students and researchers mainly focused on computer science and geography. Professionals working on spatial trajectory computing will also find this book very useful.
| Author | : Sachi Nandan Mohanty |
| Publisher | : John Wiley & Sons |
| Total Pages | : 448 |
| Release | : 2020-07-08 |
| Genre | : Computers |
| ISBN | : 1119711576 |
This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.
| Author | : Jinho Kim |
| Publisher | : Springer |
| Total Pages | : 876 |
| Release | : 2017-04-25 |
| Genre | : Computers |
| ISBN | : 3319575295 |
This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected from 458 submissions. They are organized in topical sections named: classification and deep learning; social network and graph mining; privacy-preserving mining and security/risk applications; spatio-temporal and sequential data mining; clustering and anomaly detection; recommender system; feature selection; text and opinion mining; clustering and matrix factorization; dynamic, stream data mining; novel models and algorithms; behavioral data mining; graph clustering and community detection; dimensionality reduction.
| Author | : Huiji Gao |
| Publisher | : |
| Total Pages | : 117 |
| Release | : 2014 |
| Genre | : Recommender systems (Information filtering) |
| ISBN | : |
The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation.
