Review And Implementation Of Common Statistical Methods For Recommender Systems
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Review and Implementation of Common Statistical Methods for Recommender Systems
As a result of today's massive information overload, the exploration and development of recommender systems is burgeoning. This paper consists of a comprehensive literature review in which the current knowledge surrounding statistical methods for recommender systems is outlined and evaluated. For each method, the theoretical premise and application-related aspects such as optimal use cases and common research problems are described. To round out the literature review, an implementation of several collaborative filtering techniques is conducted in order to apply the discussed theory and identify some advantages and disadvantages of the methods.
Recommender Systems
Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data. Features of this book: Identifies and describes recommender systems for practical uses Describes how to design, train, and evaluate a recommendation algorithm Explains migration from a recommendation model to a live system with users Describes utilization of the data collected from a recommender system to understand the user preferences Addresses the security aspects and ways to deal with possible attacks to build a robust system This book is aimed at researchers and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
Reviews in Recommender Systems: 2022
Frontiers in Big Data is delighted to present the ‘Reviews in Recommender Systems’ series of article collections. Reviews in Recommender Systems will publish high-quality scholarly review papers on key topics in recommender systems and their applications in our everyday lives, in search engines, online retail, news, entertainment, travel, social networks, and much more. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. We anticipate the research presented will promote discussion in the Big Data community that will translate to best practice applications in further research, industry, real-world implementations, public health, and policy settings.