Essay About Approaches Of Recommendation Systems Table Of Contentschapter And Recommender Systems

Methods and Approaches of Recommendation Systems
Methods and approaches of recommendation systems TABLE OF CONTENTSCHAPTER 1 Overview of Recommendation Systems Algorithms and Methods        Introduction        1.1 Content-based methods of recommendation construction        1.2 Collaborative methods of producing recommendations        1.3 Hybrid methods of producing recommendations        1.4 Conclusions        CHAPTER 2 Mathematical Approaches Used in Recommender Systems        2.1 Mathematical statement of collaborative filtering problem        2.2 The efficiency of collaborative filtering algorithm        2.3 Estimates of similarity of objects        2.4 Filtering based on similarity of items        2.5 Filtering based on similarity of users        2.6 Conclusions        Conclusions        References                        CHAPTER 1 Overview of Recommendation Systems Algorithms and Methods IntroductionRecommender systems use the opinions of a community of users to help individuals in that community more effectively identify content of interest from a potentially overwhelming set of choices. They have become fundamental applications in electronic commerce and information access.  Although the algorithms used within these systems vary, most are based on one or more of three classes of technology: data mining, information filtering and retrieval, and collaborative filtering.The earliest “recommender systems” were information filtering and retrieval systems designed to fight information overload in textual domains. Recommender systems that incorporate information retrieval methods are frequently used to satisfy ephemeral information needs from relatively static databases. Conversely, recommender systems that incorporate information filtering (IF) methods are frequently used to identify items that match relatively stable and specific information needs in domains with a rapid turnover or frequent additions [4]. Nowadays the technology behind recommender systems has evolved over the past 20 years into a rich collection of tools that enable the practitioner or researcher to develop effective recommenders.