Download or read book Joint Item Tag Recommendation Framework for Collaborative Filtering in Social Tagging Systems written by Jing Peng and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tapping into the wisdom of the crowd, social tagging is becoming an increasingly important mechanism for organizing and discovering information on the Web. Effective tag-based recommendation of information items is one of the key technologies contributing to the success of this social information discovery mechanism. A precise understanding of the information structure of social tagging systems lies at the core of an effective tag-based item recommendation method. While most existing methods either implicitly or explicitly assume a simple tripartite graph structure, in this paper, we propose a comprehensive data model to capture all types of co-occurrence information in the social tagging context. Based on this data model, we further propose a unified user profiling scheme to make full use of all available information. Finally, supported by this user profile, we propose a framework for collaborative filtering in social tagging systems. In this framework, we first generate joint item-tag recommendations, with tags indicating topical interests of users in target items. These joint recommendations are then refined by the wisdom from the crowd and projected to the item (or tag) space for final item (or tag) recommendations. Empirical evaluation using real-world data demonstrates the utility of our proposed approach.

Download or read book Recommender Systems for Social Tagging Systems written by Leandro Balby Marinho and published by Springer Science & Business Media. This book was released on 2012-02-10 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.

Download or read book Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems written by Robert Jäschke and published by Ios PressInc. This book was released on 2011-01-01 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most noticeable innovation that emerged with the advent of the Web 2.0 and the focal point of this thesis are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords, so called tags. The tags

Download or read book Recommender Systems and the Social Web written by Fatih Gedikli and published by Springer Science & Business Media. This book was released on 2013-03-29 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommender algorithm is proposed which recommends tags for users to annotate their favorite online resources. The author also proposes algorithms which exploit the user-provided tagging data and produce more accurate recommendations. On the basis of this idea, he shows how tags can be used to explain to the user the automatically generated recommendations in a clear and intuitively understandable form. With his book, Fatih Gedikli gives us an outlook on the next generation of recommendation systems in the Social Web sphere.

Download or read book User Modeling Adaptation and Personalization written by Vania Dimitrova and published by Springer. This book was released on 2014-06-19 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed proceedings of the 22nd International Conference on User Modeling, Adaption and Personalization, held in Aalborg, Denmark, in July 2014. The 23 long and 19 short papers of the research paper track were carefully reviewed and selected from 146 submissions. The papers cover the following topics: large scale personalization, adaptation and recommendation; Personalization for individuals, groups and populations; modeling individuals, groups and communities; Web dynamics and personalization; adaptive web-based systems; context awareness; social recommendations; user experience; user awareness and control; Affective aspects; UMAP underpinning by psychology models; privacy; perceived security and trust; behavior change and persuasion.

Download or read book CFUI written by Jing Peng and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As opposed to Web search, social tagging can be considered an alternative technique tapping into the wisdom of the crowd for organizing and discovering information on the Web. Effective tag-based recommendation of information items is critical to the success of this social information discovery mechanism. Over the past few years, there have been a growing number of studies aiming at improving the item recommendation quality of collaborative filtering (CF) methods by leveraging tagging information. However, a critical problem that often severely undermines the performance of tag-based CF methods, i.e., sparsity of user-item and user-tag interactions, is still yet to be adequately addressed. In this paper, we propose a novel learning framework, which deals with this data sparsity problem by making effective use of unlabeled items and propagating users' preference information between the item space and the tag space. Empirical evaluation using real-world tagging data demonstrates the utility of the proposed framework.

Download or read book Web Data Mining written by Bing Liu and published by Springer Science & Business Media. This book was released on 2011-06-25 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Download or read book Tag Prediction for Personalization written by Harish Mangalampalli and published by . This book was released on 2015 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative tagging systems are social data repositories in which users manage resources using descriptive keywords (tags). An important element of collaborative tagging systems is the tag recommender, which proposes a set of tags to each newly posted resource. In this paper, we propose five recommender systems for tag recommendation. The first system uses a hybrid approach that compiles a set of resource specific tags, which includes tags related to the title, tags previously used to describe the same resource (resource profile) and tags previously used to describe similar resources (related resource profile). These tags are checked against user profile tags - a rich, but imprecise source of information about user interests. The result is a set of tags related both to the resource and user. Depending on the nature of processed posts, this set can be an extension of the most common tag recommendation sources, namely the title and resource profile. The second system uses a slightly different hybrid approach originally developed by Melville et al. to predict movie ratings. It uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering (CBCF), performs better than a hybrid approach as well as other previously developed recommender systems described in this work. Three other systems are built as an extension to the CBCF approach, where a content-based preprocessing step generates a set of imprecise predictions for every user-document pair in the training dataset. This results in a dense dataset on which techniques like Association Rule Mining (ARM) can be applied to leverage the density of content in the dataset. The first of these three systems, CB_ARM, uses the same content-based preprocessing step that is used in the CBCF algorithm mentioned above. However, the collaborative filtering based recommendation step is replaced with a Weighted ARM algorithm. The second system, CB_LDA_ARM, uses Latent Dirichlet Allocation (LDA) in the content-based preprocessing step. LDA reduces the dimensionality of the dataset, post which, distance/similarity computations, required for making content-based predictions, can be performed on low dimensional data. The output of the preprocessing step is fed to the Weighted ARM algorithm mentioned above. The third system, LDA_ARM, uses LDA in the recommendation step prior to the execution of the Weighted ARM algorithm. LDA is applied to the tag sets predicted using the content-based preprocessing step. This generates a set of latent tag-topics which reduces dimensionality of the dataset by clustering tags into tag-topics. The Weighted ARM algorithm is then applied to the new dataset consisting of tag-topics and their probabilities. The most frequent tags from the topics predicted by ARM are used for expanding the existing tag set. We finally present results which show that our CB_ARM recommender system outperforms all other systems discussed in this work, when evaluated on a subset of the challenge dataset.

Download or read book Trust for Intelligent Recommendation written by Touhid Bhuiyan and published by Springer Science & Business Media. This book was released on 2013-03-30 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems are one of the recent inventions to deal with the ever-growing information overload in relation to the selection of goods and services in a global economy. Collaborative Filtering (CF) is one of the most popular techniques in recommender systems. The CF recommends items to a target user based on the preferences of a set of similar users known as the neighbors, generated from a database made up of the preferences of past users. In the absence of these ratings, trust between the users could be used to choose the neighbor for recommendation making. Better recommendations can be achieved using an inferred trust network which mimics the real world “friend of a friend” recommendations. To extend the boundaries of the neighbor, an effective trust inference technique is required. This book proposes a trust interference technique called Directed Series Parallel Graph (DSPG) that has empirically outperformed other popular trust inference algorithms, such as TidalTrust and MoleTrust. For times when reliable explicit trust data is not available, this book outlines a new method called SimTrust for developing trust networks based on a user’s interest similarity. To identify the interest similarity, a user’s personalized tagging information is used. However, particular emphasis is given in what resources the user chooses to tag, rather than the text of the tag applied. The commonalities of the resources being tagged by the users can be used to form the neighbors used in the automated recommender system. Through a series of case studies and empirical results, this book highlights the effectiveness of this tag-similarity based method over the traditional collaborative filtering approach, which typically uses rating data. Trust for Intelligent Recommendation is intended for practitioners as a reference guide for developing improved, trust-based recommender systems. Researchers in a related field will also find this book valuable.

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Download or read book A Distributed Framework for Performing Tag Based Collaborative Filtering on Recommender Systems written by and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spectral Graph Theory written by Fan R. K. Chung and published by American Mathematical Soc.. This book was released on 1997 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text discusses spectral graph theory.

Download or read book A Recommender System Using Tag based Collaborative User Model written by Abdulmajeed Alkhaldi and published by . This book was released on 2010 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advances in Data Mining Applications and Theoretical Aspects written by Petra Perner and published by Springer. This book was released on 2017-06-30 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 17th Industrial Conference on Advances in Data Mining, ICDM 2017, held in New York, NY, USA, in July 2017. The 27 revised full papers presented were carefully reviewed and selected from 71 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control in industry and society.

Download or read book Recommender Systems written by Dietmar Jannach and published by Cambridge University Press. This book was released on 2010-09-30 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.

Download or read book Social Information Access written by Peter Brusilovsky and published by Springer. This book was released on 2018-05-02 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social information access is defined as a stream of research that explores methods for organizing the past interactions of users in a community in order to provide future users with better access to information. Social information access covers a wide range of different technologies and strategies that operate on a different scale, which can range from a small closed corpus site to the whole Web. The 16 chapters included in this book provide a broad overview of modern research on social information access. In order to provide a balanced coverage, these chapters are organized by the main types of information access (i.e., social search, social navigation, and recommendation) and main sources of social information.

Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.