Probabilistic Models For Recommendation In Social Networks
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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.
Adaptive Probabilistic Topic Models For Social Networks
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Author : Arta Shayandeh
language : en
Publisher:
Release Date : 2012
Adaptive Probabilistic Topic Models For Social Networks written by Arta Shayandeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with Online social networks categories.
Online social networks such as Twitter, LinkedIn, and Facebook generate tremendous amount of text and social interaction data. On one hand, the increasing amount of available information has motivated computational research in social network analysis to understand social structures. On the other hand, annotating, retrieving, and analyzing textual information generated within the social network is also crucial for many applications such as content ranking, recommendation systems, spam detection, and viral marketing. In this thesis we propose a composite probabilistic topic model for social networks which automatically learns topic (of interest) distributions for each entity in the social network using a combination of the available content (text) in social network and the structural properties of the network. The utility of our proposed modeling is to reduce the dimensionality of the data, exploit the underlying social structure and linkage property of the network while generating a more accurate topic model for the end-users of the social network. We discuss in detail the results on both the NIPS data set (papers from the Neural Information Processing Conference) and Enron Email (emails from large corporation) corpus. We present perplexity score for test documents as a basis of our experiments to evaluate the generalization performance of our model and provide evidence that relevant topics are discovered.
Probabilistic Approaches To Recommendations
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Author : Nicola Barbieri
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2014-05-01
Probabilistic Approaches To Recommendations written by Nicola Barbieri and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-01 with Computers categories.
The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.
Probabilistic Approaches To Recommendations
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Author : Nicola Barbieri
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Probabilistic Approaches To Recommendations written by Nicola Barbieri and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the choices and preferences of users is a challenging task: real-world scenarios involve users behaving in complex situations, where prior beliefs, specific tendencies, and reciprocal influences jointly contribute to determining the preferences of users toward huge amounts of information, services, and products. Probabilistic modeling represents a robust formal mathematical framework to model these assumptions and study their effects in the recommendation process. This book starts with a brief summary of the recommendation problem and its challenges and a review of some widely used techniques Next, we introduce and discuss probabilistic approaches for modeling preference data. We focus our attention on methods based on latent factors, such as mixture models, probabilistic matrix factorization, and topic models, for explicit and implicit preference data. These methods represent a significant advance in the research and technology of recommendation. The resulting models allow us to identify complex patterns in preference data, which can be exploited to predict future purchases effectively. The extreme sparsity of preference data poses serious challenges to the modeling of user preferences, especially in the cases where few observations are available. Bayesian inference techniques elegantly address the need for regularization, and their integration with latent factor modeling helps to boost the performances of the basic techniques. We summarize the strengths and weakness of several approaches by considering two different but related evaluation perspectives, namely, rating prediction and recommendation accuracy. Furthermore, we describe how probabilistic methods based on latent factors enable the exploitation of preference patterns in novel applications beyond rating prediction or recommendation accuracy. We finally discuss the application of probabilistic techniques in two additional scenarios, characterized by the availability of side information besides preference data. In summary, the book categorizes the myriad probabilistic approaches to recommendations and provides guidelines for their adoption in real-world situations.
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.
Social Network Based Recommender Systems
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Author : Daniel Schall
language : en
Publisher: Springer
Release Date : 2015-09-23
Social Network Based Recommender Systems written by Daniel Schall and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-23 with Computers categories.
This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on ‘social brokers’ are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.
Recommender Systems For Location Based Social Networks
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Author : Panagiotis Symeonidis
language : en
Publisher: Springer Science & Business Media
Release Date : 2014-02-08
Recommender Systems For Location Based Social Networks written by Panagiotis Symeonidis 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 2014-02-08 with Computers categories.
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.
Spatio Temporal Recommendation In Social Media
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Author : Hongzhi Yin
language : en
Publisher: Springer
Release Date : 2016-05-19
Spatio Temporal Recommendation In Social Media written by Hongzhi Yin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-19 with Computers categories.
This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users’ behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users’ behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.
Data Mining For Social Network Data
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Author : Nasrullah Memon
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-10
Data Mining For Social Network Data written by Nasrullah Memon 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 2010-06-10 with Business & Economics categories.
Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.
Resolution Recommendation And Explanation In Richly Structured Social Networks
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Author : Pigi Kouki
language : en
Publisher:
Release Date : 2018
Resolution Recommendation And Explanation In Richly Structured Social Networks written by Pigi Kouki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
I formulate the problems of entity resolution, recommendation, and explanation as inference in a graphical model. To create my models and reason over the graphs, I build upon a statistical relational learning framework called probabilistic soft logic. My models, which allow for scalable, collective inference, show an improved performance over state-of-the-art methods by leveraging richly-structured data, i.e., relational features (such as user similarities), complex relationships (such as mutual exclusion), a variety of similarity measures, as well as other heterogenous data sources (such as predictions from other algorithms).