Chapter 1 Recommender Systems : Introduction and Challenges (original) (raw)
Related papers
Recommender Systems: Introduction and Challenges
Recommender Systems Handbook, 2015
Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user [17, 41, 42]. The suggestions relate to various decision-making processes, such as what items to buy, what music to listen to, or what online news to read. "Item" is the general term used to denote what the system recommends to users. An RS normally focuses on a specific type of item (e.g., CDs or news) and, accordingly its design, its graphical user interface, and the core recommendation technique used to generate the recommendations are all customized to provide useful and effective suggestions for that specific type of item. RSs are primarily directed toward individuals who lack the sufficient personal experience or competence in order to evaluate the potentially overwhelming number of alternative items that a website, for example, may offer [42]. A prime example is a book recommender system that assists users in selecting a book to read. On the popular website, Amazon.com, the site employs an RS to personalize the online store for each customer [32]. Since recommendations are usually personalized, different users or user groups benefit from diverse, tailored suggestions. In addition, there are also non-personalized recommendations. These are much simpler to generate and are normally featured in magazines or newspapers. Typical examples
Guest Editors' Introduction: Recommender Systems
IEEE Intelligent Systems, 2007
R ecommender systems support users by identifying interesting products and services in situations where the number and complexity of offers outstrips the user's capability to survey them and reach a decision. Interest in recommender systems has dramatically increased owing to the demand for personalization technologies from large, successful e-commerce applications (such as Amazon. com). Nowadays, numerous online shops employ recommender applications, which many regard as a key enabling technology of e-commerce. Corresponding applications recommend everything from news, Web sites, CDs, books, and movies to more complex items such as financial services, digital cameras, or e-government services. Recommendations are determined either by explicitly conducting sales dialogues with online users or by analyzing existing purchasing data from a single user or a community of users. Following the latter approach, the first recommender applications, developed in the mid-1990s, aimed to aggregate existing rating information to derive new user recommendations.
Recommender systems: models, challenges and opportunities, 2023
The purpose of this study is to provide a comprehensive overview of the latest developments in the field of recommender systems. In order to provide an overview of the current state of affairs in this sector and highlight the latest developments in recommender systems, the research papers available in this area were analyzed. The place of recommender systems in the modern world was defined, their relevance and role in people's daily lives in the modern information environment were highlighted. The advantages of recommender systems and their main properties are considered. In order to formally define the concept of recommender systems, a general scheme of recommender systems was provided and a formal task was formulated. A review of different types of recommender systems is carried out. It has been determined that personalized recommender systems can be divided into content filtering-based systems, collaborative filtering-based systems, and hybrid recommender systems. For each type of system, the author defines them and reviews the latest relevant research papers on a particular type of recommender system. The challenges faced by modern recommender systems are separately considered. It is determined that such challenges include the issue of robustness of recommender systems (the ability of the system to withstand various attacks), the issue of data bias (a set of various data factors that lead to a decrease in the effectiveness of the recommender system), and the issue of fairness, which is related to discrimination against users of recommender systems. Overall, this study not only provides a comprehensive explanation of recommender systems, but also provides information to a large number of researchers interested in recommender systems. This goal was achieved by analyzing a wide range of technologies and trends in the service sector, which are areas where recommender systems are used.
2016
– Recommender systems are a vital part of today’s information society to deal with information overload, especially in e-commerce. Recommender systems help retailers to choose items to display based on customers’ preferences, help users to search for items in personalized ways, and help streaming services create customized playlists. This entry describes multiple kinds of recommender systems and how they work. It also explains their historical and intellectual context, shows how they might affect users, and discusses current challenges.
Introduction to recommender systems handbook
Recommender Systems Handbook, 2011
Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. In this introductory chapter we briefly discuss basic RS ideas and concepts. Our main goal is to delineate, in a coherent and structured way, the chapters included in this handbook and to help the reader navigate the extremely rich and detailed content that the handbook offers.
Recommender Systems: An Overview
AI Magazine
Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and id...
Recommender Systems: Past, Present, Future
AI Magazine, 2021
The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the...
Recommender Systems: Issues, Challenges, and Research Opportunities
A recommender system is an Information Retrieval technology that improves access and proactively recommends relevant items to users by considering the users' explicitly mentioned preferences and objective behaviors. A recommender system is one of the major techniques that handle information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. Today, several recommender systems have been developed for different domains however, these are not precise enough to fulfil the information needs of users. Therefore, it is necessary to build high quality recommender systems. In designing such recommenders, designers face several issues and challenges that need proper attention. This paper investigates and reports the current trends, issues, challenges, and research opportunities in developing high-quality recommender systems. If properly followed, these issues and challenges will introduce new research avenues and the goal towards fine-tuned and high-quality recommender systems can be achieved.
Recommender Systems Review of Types, Techniques, and Applications
Encyclopedia of Information Science and Technology, Third Edition, 2015
Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or actions during interaction with an application or website. They are usually personalized and can refer to items to buy, people to connect to or books/ articles to read. Recommender Systems (RS) aim at helping users with their interaction by bringing to surface the information that is relevant to them, their needs, or their tasks. This chapter's objective is to present a review of the different types of RS, the techniques and methods used for building such systems, the algorithms used to generate the recommendations and how these systems can be evaluated. Finally, a number of topics are discussed as envisioned future research directions.
A Review of Recommender Systems: Types, Techniques and Applications
Recommender or recommendation systems are software tools that make useful suggestions to users, by taking into account their profile, preferences and/or actions during interaction with an application or website. They are usually personalized and can refer to items to buy, people to connect to or books/ articles to read. Recommender Systems (RS) aim at helping users with their interaction by bringing to surface the information that is relevant to them, their needs, or their tasks. This article's objective is to present a review of the different types of RS, the techniques and methods used for building such systems, the algorithms used to generate the recommendations and how these systems can be evaluated. Finally, a number of topics are discussed as envisioned future research directions.