Automated recommender systems make product suggestions that are tailored to the individual needs of the user and represent powerful means to combat information glut. However, their practical applicability has been largely confined to scenarios where information relevant for recommendation making is kept in one single, authoritative node. Recently, novel distributed infrastructures are emerging, e.g., peer-to-peer networks and the Semantic Web, which could likewise benefit from recommender system services, leading to a paradigm shift towards decentralized recommender systems. In this book, we investigate the challenges that decentralized recommenders bring up and propose techniques to cope with those issues. The spectrum ranges from the use of product classification taxonomies, alleviating the sparsity problem, to trust propagation mechanisms designed to address the scalability issue. Empirical investigations on the correlation of interpersonal trust and interest similarity provide the component glue that melds these results.The book is geared towards academic readers and practitioners alike, with a focus on both implementable algorithms as well as new socio-psychological insights.
Automated recommender systems make product suggestions that are tailored to the individual needs of the user and represent powerful means to combat information glut. However, their practical applicability has been largely confined to scenarios where information relevant for recommendation making is kept in one single, authoritative node. Recently, novel distributed infrastructures are emerging, e.g., peer-to-peer networks and the Semantic Web, which could likewise benefit from recommender system services, leading to a paradigm shift towards decentralized recommender systems. In this book, we investigate the challenges that decentralized recommenders bring up and propose techniques to cope with those issues. The spectrum ranges from the use of product classification taxonomies, alleviating the sparsity problem, to trust propagation mechanisms designed to address the scalability issue. Empirical investigations on the correlation of interpersonal trust and interest similarity provide the component glue that melds these results.The book is geared towards academic readers and practitioners alike, with a focus on both implementable algorithms as well as new socio-psychological insights.
Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve...
The contribution of the Hybrid Optimization driven RideNN for Software Reusability Estimation is that the C-RideNN algorithm uses the current Cat Swarm Optimization (CSO) together with the Rider...
The book explores the complete system perspective, underlying theories, modelling, and the applications of pattern recognition in Healthcare Recommender System. Considering the interest of...
This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts:...
Information is an element of knowledge that can be stored, processed or transmitted. It is linked to concepts of communication, data, knowledge or representation. In a context of steady...
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