{"product_id":"154478","title":"Introduction to Recommender Systems ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYDPL2ODvEsAueiCu0X6pDqXtB25t.png?v=1765080289\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e Introduction to Recommender Systems \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/118625987\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eIf you are considering introducing a recommendation system\u003cbr\u003e The first book you should read!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Recommendation features such as ‘tailored videos,’ ‘follow recommendations,’ and ‘products viewed by other customers’ are included in various services around us.\u003cbr\u003e Recommendation systems are essential for services that handle a large number of items, as it takes too much time to choose what you want from a large number of options.\u003cbr\u003e This book is a must-read for readers or organizations looking to implement a recommendation system. \u003cbr\u003eThe authors, who are recommendation system developers, examined success and failure cases they experienced, focusing on how to combine and apply recommendation systems to which services.\u003cbr\u003e Instead of delving into the details of the recommendation algorithm, we will focus on an overview of the algorithm and how to apply it in practice.\u003cbr\u003e This book will help you develop the right recommendation system to further evolve your service.\u003cbr\u003e\n\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eCHAPTER 1 RECOMMENDATION SYSTEM\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _1.1 Recommendation System\u003cbr\u003e _1.2 History of Recommendation Systems\u003cbr\u003e _1.3 Types of Recommendation Systems\u003cbr\u003e _1.4 Search and Recommendation Systems\u003cbr\u003e _1.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2: RECOMMENDATION SYSTEM PROJECT\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _2.1 Three skills needed to develop a recommendation system\u003cbr\u003e _2.2 How to proceed with the recommendation system project\u003cbr\u003e _2.3 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 3 UI\/UX of the Recommendation System\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _3.1 The Importance of UI\/UX \u003cbr\u003e_3.2 UI\/UX examples suitable for the purpose of users using the service\u003cbr\u003e _3.3 UI\/UX cases that fit the service provider's purpose\u003cbr\u003e _3.4 Related Topics\u003cbr\u003e _3.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 4: Overview of Recommendation Algorithms\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _4.1 Recommendation Algorithm Classification\u003cbr\u003e _4.2 Content-based filtering\u003cbr\u003e _4.3 Collaborative Filtering\u003cbr\u003e _4.4 Comparison of Content-Based Filtering and Collaborative Filtering\u003cbr\u003e _4.5 Selecting a Recommendation Algorithm\u003cbr\u003e _4.6 Characteristics of symbolic data\u003cbr\u003e _4.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5: RECOMMENDATION ALGORITHM DETAILS\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _5.1 Algorithm Comparison\u003cbr\u003e _5.2 MovieLens dataset\u003cbr\u003e _5.3 Random Recommendation\u003cbr\u003e _5.4 Recommendations based on statistical information or specific rules\u003cbr\u003e _5.5 Association Rules\u003cbr\u003e _5.6 User-User Memory-Based Method Collaborative Filtering\u003cbr\u003e _5.7 Regression Model\u003cbr\u003e _5.8 Matrix Decomposition\u003cbr\u003e _5.9 Recommendation System Applications to Natural Language Processing Methods\u003cbr\u003e _5.10 Deep Learning\u003cbr\u003e _5.11 Slot Machine Algorithm (Bandit Algorithm)\u003cbr\u003e _5.12 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6 COMBINATION WITH REAL SYSTEMS\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _6.1 System Overview\u003cbr\u003e _6.2 Log Design\u003cbr\u003e _6.3 Real System Example\u003cbr\u003e _6.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7 Evaluating Recommender Systems\u003cbr\u003e\u003c\/b\u003e \u003cbr\u003e_7.1 Three Evaluation Methods\u003cbr\u003e _7.2 Offline Evaluation\u003cbr\u003e _7.3 Online Evaluation\u003cbr\u003e _7.4 Evaluation through user studies\u003cbr\u003e _7.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 Developmental Topics\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e _8.1 International Conference\u003cbr\u003e _8.2 Bias\u003cbr\u003e _8.3 Mutual Recommendation System\u003cbr\u003e _8.4 Uplift Modeling\u003cbr\u003e _8.5 Characteristics and challenges by domain\u003cbr\u003e _8.6 Summary\u003cbr\u003e\u003cbr\u003e APPENDIX A Netflix Prize\u003cbr\u003e _A.1 Netflix Founding\u003cbr\u003e _A.2 Recommendation System Development\u003cbr\u003e _A.3 Netflix Prize\u003cbr\u003e _A.4 Netflix's recommendation system\u003cbr\u003e _A.5 Summary\u003cbr\u003e\u003cbr\u003e APPENDIX B User-to-user memory-based methods\u003cbr\u003e _B.1 Recommendation Process (1): Finding Users with Similar Taste Tendencies to You\u003cbr\u003e _B.2 Recommended Process (2): Calculating the Predicted Average Value\u003cbr\u003e _B.3 Recommendation Process (3): Recommending to Users\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate4187\/MidCate001\/418601162.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eLearn the know-how of a professional recommendation system developer! A guide to introducing a recommendation algorithm that knows you better than you know yourself.\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e In fact, recommendation systems have been around for a long time. \u003cbr\u003eA restaurant's recommended menu and a bookstore's ranking of popular books are also types of recommendation systems.\u003cbr\u003e As the number of decisions we have to make in our daily lives increases and the variety of choices we have, the demand for recommendation systems is growing.\u003cbr\u003e Recommendation algorithms have also advanced by leaps and bounds, moving beyond the standardized method of recommending popular items to provide personalized recommendations tailored to each individual's interests and preferences.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Showing users their preferred items quickly increases user satisfaction, leading to increased sales and membership.\u003cbr\u003e However, when actually applying it to services, various problems are encountered. \u003cbr\u003eThese include how to best structure the project, which recommendation systems to combine, what data to use, how to best present recommendation results, and how to evaluate the recommendation systems online before deployment.\u003cbr\u003e This book addresses precisely those concerns.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e Recommender systems are no longer a “nice-to-have” feature, but a “must-have.”\u003cbr\u003e Learn the introductory know-how from authors with experience building real-world recommendation systems.\u003cbr\u003e This will help create a recommendation system that supports users' decision-making by selecting valuable items among various items.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eContent structure\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e [Chapter 1 Recommendation System]\u003cbr\u003e We will provide an overview and history of recommender systems with some examples.\u003cbr\u003e We will also briefly look at the types of recommendation systems and explain their differences from search systems.\u003cbr\u003e \u003cbr\u003e[Chapter 2 Recommendation System Project]\u003cbr\u003e We describe the team members required to develop a recommendation system and how the project will proceed.\u003cbr\u003e\u003cbr\u003e [Chapter 3: Recommendation System UI\/UX]\u003cbr\u003e Introducing the UI\/UX of the recommendation system.\u003cbr\u003e User experience design is important because how you present recommended items can increase clicks and purchases.\u003cbr\u003e\u003cbr\u003e [Chapter 4: Overview of Recommendation Algorithms]\u003cbr\u003e We explain representative recommendation algorithms, collaborative filtering and content-based recommendation.\u003cbr\u003e And we introduce the average value data input to the recommendation algorithm by dividing it into implicit and explicit.\u003cbr\u003e\u003cbr\u003e [Chapter 5: Recommendation Algorithm Details]\u003cbr\u003e We will explain the popularity recommendation and matrix analysis algorithms separately, and also look at what to watch out for when combining them in real-world services.\u003cbr\u003e We also introduce the code that applies each algorithm using a movie dataset called MovieLens.\u003cbr\u003e\u003cbr\u003e [Chapter 6: Combination with Real Systems] \u003cbr\u003eUsing a news delivery recommendation system as an example, we explain how the system is structured when incorporating recommendation algorithms into real-world services.\u003cbr\u003e We will look at the recommendation system architecture, including server configuration, batch processing structure, and log design.\u003cbr\u003e\u003cbr\u003e [Chapter 7: Recommendation System Evaluation]\u003cbr\u003e Describes various evaluation metrics for recommender systems.\u003cbr\u003e In addition to simple metrics like prediction error, we also look at metrics that measure the diversity of recommended items and measures unexpectedness.\u003cbr\u003e\u003cbr\u003e [Chapter 8 Developmental Topics]\u003cbr\u003e We will look at the International Conference on Recommender Systems, bias removal, and causal inference, which were not previously covered.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Developers and data scientists who want to analyze data and provide customized services to each customer.\u003cbr\u003e Developers and planners who want to learn the basics of integrating recommendation systems into their work systems.\u003cbr\u003e Product managers and planners who need to communicate with developers to develop recommendation systems \u003cbr\u003eㆍUI\/UX designer responsible for the user experience of the recommended service \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e May 8, 2023\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 296 pages | 183*235*20mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169210980\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1169210988 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default 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