Content-Based Recommender Systems Taxonomy (original) (raw)
Related papers
Content-based recommender systems: State of the art and trends
Recommender Systems Handbook, 2011
Recommender systems have the effect of guiding users in a personalized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recommender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of contentbased recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.
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
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.
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.
Chapter 1 Recommender Systems : Introduction and Challenges
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 ...
Collaborative filtering recommender systems taxonomy
Knowledge and Information Systems, 2022
In the era of internet access, recommender systems try to alleviate the difficulty that consumers face while trying to find items (e.g., services, products, or information) that better match their needs. To do so, a recommender system selects and proposes (possibly unknown) items that may be of interest to some candidate consumer, by predicting her/his preference for this item. Given the diversity of needs between consumers and the enormous variety of items to be recommended, a large set of approaches have been proposed by the research community. This paper provides a review of the approaches proposed in the entire research area of collaborative filtering recommend systems. To facilitate understanding, we provide a categorization of each approach based on the tools and techniques employed, which results to the main contribution of this paper, a collaborative filtering recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of collaborative filtering recommender systems according to their ability to efficiently handle well-known drawbacks.
2015
While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. In recent years, competitions like the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on advances in collaborative filtering and how to utilize ratings and usage data. However, there are many domains where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we do not know if and how these data sources should be combined to provide the best recommendation performance. The CBRecSys workshop series aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation. The first edition in Silicon Valley in 2014, and the second one in Vienna were a big success. For the third edition, CBRecSys 2016, we once again issued a call for papers asking for submissions of novel research papers addressing recommendation in domains where textual content is abundant (e.g., books, news, scientific articles, jobs, educational resources, Web pages, etc.) as well as dedicated comparisons of contentbased techniques with collaborative filtering in different domains. Other relevant topics included opinion mining for text/book recommendation, semantic recommendation, content-based recommendation to alleviate cold-start problems, deep learning for content representation, as well as serendipity, diversity and cross-domain recommendation. Each submission was rewiewed by three members of the program committee consisting of experts in the field of recommender systems and information retrieval. We selected 9 papers from the 14 submissions for presentation at the workshop. We are also happy to have Prof. Barry Smyth of the University College Dublin and Prof. Bamshad Mobasher of the DePaul Univesity as keynote speakers.
Workshop on new trends in content-based recommender systems
Proceedings of the 8th ACM Conference on Recommender systems, 2014
While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2014 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.
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...