top of page

December Annual Holiday Party 2022! Group

Public·72 members
Joshua James
Joshua James

Recommender Systems: The Textbook __EXCLUSIVE__


This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories:




Recommender Systems: The Textbook


Download File: https://www.google.com/url?q=https%3A%2F%2Fgohhs.com%2F2tTyxD&sa=D&sntz=1&usg=AOvVaw0c9EYVQ0ZzHj6cYJg2mdac



Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.


If you are new to recommender systems, a text-book may be a good starting point. We would particularly recommend the following three textbooks. Book descriptions are from Amazon, sometimes extended by our own comments.


Written by some of the most distinguished professors in the recommender-system community, namely Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich, with the first three authors all being on the steering committee of the ACM Conference on Recommender Systems. Even though the book is a bit outdated (from 2010) it still covers all the basics and is a worthwhile read. Auxiliary materials such as lecture slides for the book are available on


In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.


Written by Charu C. Aggarwal who is not as well known in the (academic) community as the authors of the previous two books. However, he has a strong recommender-systems industry background, working at IBM, and being the author of numerous books relating to deep learning and data mining. For more details on the author see


This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: 1. Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. 2. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. 3. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.


  • Springer Shop

  • Amazon.com


  • Find in a library

  • All sellers

_OC_InitNavbar("child_node":["title":"My library","url":" =114584440181414684107\u0026source=gbs_lp_bookshelf_list","id":"my_library","collapsed":true,"title":"My History","url":"","id":"my_history","collapsed":true,"title":"Books on Google Play","url":" ","id":"ebookstore","collapsed":true],"highlighted_node_id":"");Recommender Systems: The TextbookCharu C. AggarwalSpringer, Mar 28, 2016 - Computers - 498 pages 0 ReviewsReviews aren't verified, but Google checks for and removes fake content when it's identifiedThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories:


  • Amazon.com


  • Find in a library

  • All sellers

_OC_InitNavbar("child_node":["title":"My library","url":" =114584440181414684107\u0026source=gbs_lp_bookshelf_list","id":"my_library","collapsed":true,"title":"My History","url":"","id":"my_history","collapsed":true,"title":"Books on Google Play","url":" ","id":"ebookstore","collapsed":true],"highlighted_node_id":"");Recommender Systems: The TextbookCharu C. AggarwalSpringer International Publishing, Apr 4, 2016 - Computers - 498 pages 0 ReviewsReviews aren't verified, but Google checks for and removes fake content when it's identifiedThis book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories:


This book covers the topic of recommender systems comprehensively, starting with the fundamentals and then exploring the advanced topics. The chapters of this book can be organized into three categories:


Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation.


Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as multi-armed bandits, learning to rank, group systems, multi-criteria systems, and active learning systems, are discussed together with applications.


Although this book is primarily written as a textbook, it is recognized that a large portion of the audience will comprise industrial practitioners and researchers. Therefore, the book is also designed to be useful from an applied and reference point of view. Numerous examples and exercises have been provided.


The first recommender system was created in the 1970s, in the research community at Duke University; it was then developed by Xerox Palo Alto Research Center. When the Internet came into existence in the 1990s, recommender systems were immediately adopted as the way to help people select the most suitable products from a plethora of available options.


Since then, recommender systems have become more and more popular, and they now play a critical role for big Internet companies such as Facebook, Amazon, Netflix, Google, YouTube, and Tripadvisor, venturing into the realms of social networking, entertainment, e-commerce, tourism, matchmaking, and more.


The most obvious operational goal of using a personalized recommender system is to recommend items that are relevant to the user, as people are more likely to buy items they find attractive. Recommenders need to achieve four secondary goals:


Not all recommender systems have a hard requirement of a feature store; however, when DS/ML engineers are working on multiple machine learning initiatives, manual feature engineering could cause redundancies.


The basic models for recommender systems work with two kinds of data: user-item interactions, such as ratings and buying behavior, and attribute information about users and items, such as textual profiles and relevant keywords.


In content-based recommender systems, content plays a primary role in the recommendation process. Item descriptions and attributes are leveraged in order to calculate item similarity. In this context, the user-ratings matrix above is replaced by an item-content matrix with items in the rows and item attributes in the columns. 350c69d7ab


About

Welcome to the group! You can connect with other members, ge...

Members

  • mjgreaves45
  • brob brob
    brob brob
  • Kibros Kib
    Kibros Kib
  • werder werder
    werder werder
  • H
    hp.Lidia047199
bottom of page