Recommender Systems: The Textbook by Charu C. Aggarwal

By Charu C. Aggarwal

This e-book comprehensively covers the subject of recommender structures, which supply custom-made ideas of goods or providers to clients in accordance with their prior searches or purchases. Recommender method tools were tailored to various functions together with question log mining, social networking, information concepts, and computational advertisements. This publication synthesizes either primary and complicated themes of a learn zone that has now reached adulthood. The chapters of this e-book are equipped into 3 categories:

- Algorithms and assessment: those chapters talk about the basic algorithms in recommender platforms, together with collaborative filtering equipment, content-based tools, knowledge-based equipment, ensemble-based equipment, and evaluation.

- thoughts in particular domain names and contexts: the context of a suggestion will be seen as very important part info that is affecting the advice objectives. forms of context akin to temporal info, spatial info, social information, tagging information, and trustworthiness are explored.

- complex themes and purposes: numerous robustness elements of recommender platforms, similar to shilling structures, assault versions, and their defenses are discussed.

In addition, fresh subject matters, reminiscent of studying to rank, multi-armed bandits, staff platforms, multi-criteria platforms, and lively studying structures, are brought including applications.

even supposing this booklet basically serves as a textbook, it is going to additionally attract commercial practitioners and researchers as a result of its specialise in functions and references. a variety of examples and workouts were supplied, and an answer handbook is on the market for instructors.

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We will also explore a number of specific applications, such as news recommendations and computational advertising. It is hoped that this book will provide a comprehensive overview and understanding of the different scenarios that arise in the field of recommender systems. 7 Bibliographic Notes Recommender systems became increasingly popular in the mid-nineties, as recommendation systems such as GroupLens [501] were developed. Since then, this topic has been explored extensively in the context of a wide variety of models such as collaborative systems, contentbased systems, and knowledge-based systems.

The main challenge in designing collaborative filtering methods is that the underlying ratings matrices are sparse. Consider an example of a movie application in which users specify ratings indicating their like or dislike of specific movies. Most users would have viewed only a small fraction of the large universe of available movies. As a result, most of the ratings are unspecified. The specified ratings are also referred to as observed ratings. Throughout this book, the terms “specified” and “observed” will be used in an interchangeable way.

User-specific locality: The geographical location of a user has an important role in her preferences. For example, a user from Wisconsin might not have the same movie preferences as a user from New York. This type of locality is referred to as preference locality. com CHAPTER 1. AN INTRODUCTION TO RECOMMENDER SYSTEMS 22 2. , restaurant) might have an impact on the relevance of the item, depending on the current location of the user. Users are generally not willing to travel very far from their current location.

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