000			02659nam a22003017a 4500
003			IIITD
005			20250219165908.0
008			250218b |||||||| |||| 00| 0 eng d
020			_a9781032840826
040			_aIIITD
082			_a005.56 _bMAJ-C
100			_aMajumdar, Angshul
245			_aCollaborative filtering : _brecommender systems _cby Angshul Majumdar
260			_aLondon : _bCRC Press, _c©2025
300			_axiii, 127 p. : _bill. ; _c24 cm.
500			_aIncludes index.
505			_t1. Introduction and Organization
505			_t2. Neighborhood-Based Models
505			_t3. Ratings
505			_t4. Latent Factor Models
505			_t5. Using Metadata
505			_t6. Diversity in Recommender Systems
505			_t7. Deep Latent Factor Models
505			_t8. Conclusion and Note to Instructors
520			_aThis book dives into the inner workings of recommender systems, those ubiquitous technologies that shape our online experiences. From Netflix show suggestions to personalized product recommendations on Amazon or the endless stream of curated YouTube videos, these systems power the choices we see every day. Collaborative filtering reigns supreme as the dominant approach behind recommender systems. This book offers a comprehensive exploration of this topic, starting with memory-based techniques. These methods, known for their ease of understanding and implementation, provide a solid foundation for understanding collaborative filtering. As you progress, you'll delve into latent factor models, the abstract and mathematical engines driving modern recommender systems. The journey continues with exploring the concepts of metadata and diversity. You'll discover how metadata, the additional information gathered by the system, can be harnessed to refine recommendations. Additionally, the book delves into techniques for promoting diversity, ensuring a well-balanced selection of recommendations. Finally, the book concludes with a discussion of cutting-edge deep learning models used in recommender systems. This book caters to a dual audience. First, it serves as a primer for practicing IT professionals or data scientists eager to explore the realm of recommender systems. The book assumes a basic understanding of linear algebra and optimization but requires no prior knowledge of machine learning or programming. This makes it an accessible read for those seeking to enter this exciting field. Second, the book can be used as a textbook for a graduate-level course. To facilitate this, the final chapter provides instructors with a potential course plan.
650			_aTechnology
942			_2ddc _cBK
999			_c189886 _d189886