Exploring Social Networks For Collaborative Recommendation
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Exploring Social Networks For Collaborative Recommendation
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Author : Bin Liu
language : en
Publisher:
Release Date : 2011
Exploring Social Networks For Collaborative Recommendation written by Bin Liu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.
Collaboration And The Semantic Web Social Networks Knowledge Networks And Knowledge Resources
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Author : Brüggemann, Stefan
language : en
Publisher: IGI Global
Release Date : 2012-04-30
Collaboration And The Semantic Web Social Networks Knowledge Networks And Knowledge Resources written by Brüggemann, Stefan and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-30 with Computers categories.
Collaborative working has been increasingly viewed as a good practice for organizations to achieve efficiency. Organizations that work well in collaboration may have access to new sources of funding, deliver new, improved, and more integrated services, make savings on shared costs, and exchange knowledge, information and expertise. Collaboration and the Semantic Web: Social Networks, Knowledge Networks and Knowledge Resources showcases cutting-edge research on the intersections of Semantic Web, collaborative work, and social media research, exploring how the resources of so-called social networking applications, which bring people together to interact and encourage sharing of personal information and ideas, can be tapped by Semantic Web techniques, making shared Web contents readable and processable for machine and intelligent applications, as well as humans. Semantic technologies have shown their potential for integrating valuable knowledge, and they are being applied to the composition of digital learning and working platforms. Integrated semantic applications, linked data, social networks, and networked digital solutions can now be used in collaborative environments and present participants with the context-aware information that they need.
Virtual Communities Social Networks And Collaboration
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Author : Athina A. Lazakidou
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-06-20
Virtual Communities Social Networks And Collaboration written by Athina A. Lazakidou and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-20 with Computers categories.
Online communities are among the most obvious manifestations of social networks based on new media technology. Facilitating ad-hoc communication and leveraging collective intelligence by matching similar or related users have become important success factors in almost every successful business plan. Researchers are just beginning to understand virtual communities and collaborations among participants currently proliferating across the world. Virtual Communities, Social Networks and Collaboration covers cutting edge research topics of utmost real-world importance in the specific domain of social networks. This volume focuses on exploring issues relating to the design, development, and outcomes from electronic groups and online communities, including: - The implications of social networking, - Understanding of how and why knowledge is shared among participants, - What leads to participation, effective collaboration, co-creation and innovation, - How organizations can better utilize the potential benefits of communities in both internal operations, marketing, and new product development.
Advances In Conceptual Modeling Applications And Challenges
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Author : Juan Trujillo
language : en
Publisher: Springer
Release Date : 2010-10-19
Advances In Conceptual Modeling Applications And Challenges written by Juan Trujillo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-19 with Computers categories.
This book constitutes the refereed proceedings of workshops, held at the 29th International Conference on Conceptual Modeling, ER 2010, in Vancouver, Canada, in November 2010. The 31 revised full papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in sections on the workshops Semantic and Conceptual Issues in GIS (SeCoGIS); Conceptual Modeling of Life Sciences Applications (CMLSA); Conceptual Modelling of Services (CMS); Active Conceptual Modeling of Learning (ACM-L); Web Information Systems Modeling (WISM); Domain Engineering (DE@ER); and Foundations and Practices of UML (FP-UML).
Recommendation And Search In Social Networks
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Author : Özgür Ulusoy
language : en
Publisher: Springer
Release Date : 2015-02-12
Recommendation And Search In Social Networks written by Özgür Ulusoy and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-12 with Computers categories.
This edited volume offers a clear in-depth overview of research covering a variety of issues in social search and recommendation systems. Within the broader context of social network analysis it focuses on important and up-coming topics such as real-time event data collection, frequent-sharing pattern mining, improvement of computer-mediated communication, social tagging information, search system personalization, new detection mechanisms for the identification of online user groups, and many more. The twelve contributed chapters are extended versions of conference papers as well as completely new invited chapters in the field of social search and recommendation systems. This first-of-its kind survey of current methods will be of interest to researchers from both academia and industry working in the field of social networks.
Probabilistic Models For Recommendation In Social Networks
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Author : SeyedMohsen Jamali
language : en
Publisher:
Release Date : 2013
Probabilistic Models For Recommendation In Social Networks written by SeyedMohsen Jamali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Online social networks categories.
Recommender systems are becoming tools of choice to select the online information relevant to a given user. Collaborative filtering is the most popular approach to building recommender systems and has been successfully employed in many applications. However, collaborative filtering based approaches perform poorly for so-called cold start users. With the advent of online social networks, the social network based approach to recommendation has emerged. This approach assumes a social network among users and makes recommendations for a user based on the ratings of the users that have direct or indirect social relations with the given user. As one of their major benefits, social network based approaches have been shown to reduce the problems with cold start users. In this research we propose novel methods to address the recommendation problem in online social networks. To better understand the underlying mechanisms of user behavior in a social network, we first propose a model to capture the temporal dynamics of user behavior based on different effects influencing the behavior of users in rating items and creating social relations (e.g. social influence, social selection and transitivity of relations). Then we propose a memory based approach based on random walk models to perform recommendation in social networks. Matrix factorization is the most prominent model based approach for collaborative recommendation. We extend matrix factorization and propose a model that takes into account the social network as well as the rating matrix. Finally, we present a mixed membership community based model for recommendation in social networks based on stochastic block models. This model is capable of performing both rating and link prediction. All methods have been experimentally evaluated and compared against state-of-the-art methods on real life data sets from Epinions.com, Flixster.com and Flickr.com. The Flixster data set has been crawled and published as part of the research during this thesis. Experimental results show that our proposed models achieve substantial quality gains compared to the existing methods.
Point Of Interest Recommendation In Location Based Social Networks
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Author : Shenglin Zhao
language : en
Publisher: Springer
Release Date : 2018-07-13
Point Of Interest Recommendation In Location Based Social Networks written by Shenglin Zhao and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-13 with Computers categories.
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.
Computational Social Network Analysis
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Author : Ajith Abraham
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-12-10
Computational Social Network Analysis written by Ajith Abraham and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-12-10 with Computers categories.
Social networks provide a powerful abstraction of the structure and dynamics of diverse kinds of people or people-to-technology interaction. Web 2.0 has enabled a new generation of web-based communities, social networks, and folksonomies to facilitate collaboration among different communities. This unique text/reference compares and contrasts the ethological approach to social behavior in animals with web-based evidence of social interaction, perceptual learning, information granulation, the behavior of humans and affinities between web-based social networks. An international team of leading experts present the latest advances of various topics in intelligent-social-networks and illustrates how organizations can gain competitive advantages by applying the different emergent techniques in real-world scenarios. The work incorporates experience reports, survey articles, and intelligence techniques and theories with specific network technology problems. Topics and Features: Provides an overview social network tools, and explores methods for discovering key players in social networks, designing self-organizing search systems, and clustering blog sites, surveys techniques for exploratory analysis and text mining of social networks, approaches to tracking online community interaction, and examines how the topological features of a system affects the flow of information, reviews the models of network evolution, covering scientific co-citation networks, nature-inspired frameworks, latent social networks in e-Learning systems, and compound communities, examines the relationship between the intent of web pages, their architecture and the communities who take part in their usage and creation, discusses team selection based on members’ social context, presents social network applications, including music recommendation and face recognition in photographs, explores the use of social networks in web services that focus on the discovery stage in the life cycle of these web services. This useful and comprehensive volume will be indispensible to senior undergraduate and postgraduate students taking courses in Social Intelligence, as well as to researchers, developers, and postgraduates interested in intelligent-social-networks research and related areas.
Recommendation In Social Media
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Author : Xin Wang
language : en
Publisher:
Release Date : 2017
Recommendation In Social Media written by Xin Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Recommender systems are ubiquitous in our digital life in recent years. They play a significant role in numerous Internet services and applications such as electronic commerce (Amazon and eBay), on-demand video streaming (Netflix and Hulu). A key task in recommender systems is to model user preferences and to suggest, for each user, a personalized list of items that the user has not experienced, but are deemed highly relevant to her. Many of these recommendation algorithms are based on the principle of collaborative filtering, suggesting items that similar users have consumed. With the advent of online social networks, social recommendation has become one of the most popular research topics in recommender systems, exploiting the effects of social influence and selection in social networks, where user relationships are explicit, i.e., there will be an edge connecting two users if they are friends. In addition, more information about the relationships between users in social media becomes available with the rapid development of various Internet services. For example, more and more online web services are providing mechanisms by which users can self-organize into groups with other users having similar opinions or interests, enabling us to analyze the interactions between users with others insides/outsides groups, as well as the engagement between users and groups. User relationships in these applications are usually implicit and can only be utilized indirectly for recommendation tasks. In this thesis, we focus on utilizing user relationships (either explicit or implicit) to enhance personalized recommendation in social media. We study three problems of recommendation in social media, i.e., recommendation with strong and weak ties, social group recommendation and interactive social recommendation in an online setting. We propose to improve social recommendation by incorporating the concept of strong and weak ties which are two well documented terms in the social sciences, boost the performance of social group recommendation through modeling the temporal dynamics of engagement of users with groups, and tackle the interactive social recommendation problem via employing the exploitation-exploration strategy in an online setting. Our proposed models are all compared with state-of-the-art baselines on several real-world datasets.
Mining Human Mobility In Location Based Social Networks
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Author : Huiji Gao
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Mining Human Mobility In Location Based Social Networks written by Huiji Gao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
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.