<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the era of internet access, recommender systems try to alleviate the difficulty 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 content-based recommender systems, and not only in one part of it. 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 content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks.</p>
</abstract>

In the era of internet access, recommender systems try to alleviate the difficulty 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 content-based recommender systems, and not only in one part of it. 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 content-based recommender systems taxonomy. This way, the reader acquires a quick and complete understanding of this research area. Finally, we provide a comparison of content-based recommender systems according to their ability to efficiently handle well-known drawbacks.

Content-Based Recommender Systems Taxonomy

Department of Electrical and Computer Engineering

Department of Management Science and Technology

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Content-Based Recommender Systems Taxonomy"}

In the era of internet access, recommender systems try to alleviate the difficulty consumers face while trying to find items (e.g. services, products, or...

Content-Based Recommender Systems Taxonomy

Published Online: Jun 30, 2023

Page range: 211 - 241

Received: Jul 13, 2022

Accepted: Oct 12, 2022

DOI: https://doi.org/10.2478/fcds-2023-0009

Keywords
content-based systems, recommendation systems, survey, taxonomy

© 2023 Harris Papadakis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Content-Based Recommender Systems Taxonomy

Published Online: Jun 30, 2023

Page range: 211 - 241

Received: Jul 13, 2022

Accepted: Oct 12, 2022

DOI: https://doi.org/10.2478/fcds-2023-0009

Keywordscontent-based systems, recommendation systems, survey, taxonomy

© 2023 Harris Papadakis et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
content-based systems, recommendation systems, survey, taxonomy