The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Recommendation Systems
|
|
Author(s): Houda El Bouhissi (LIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, Algeria)
Copyright: 2023
Pages: 17
Source title:
Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch169
Purchase
|
Abstract
With the increasing amount of data produced daily, it becomes very difficult for users to find resources suitable to their needs. Recommendation systems, which are capable of providing individualized suggestions or guiding the user to interesting or relevant resources within a large data space, are proposed for this purpose. In this article, the authors do a comprehensive assessment of recommendation models, propose a categorization scheme, analyze challenges, and explore unresolved issues. In addition, they highlight new trends and future visions in this field of study, emphasizing the need of merging ontologies and machine-learning algorithms to increase the accuracy and efficiency of the recommender systems.
Related Content
|
Princy Pappachan, Sreerakuvandana, Mosiur Rahaman.
© 2024.
26 pages.
|
|
Winfred Yaokumah, Charity Y. M. Baidoo, Ebenezer Owusu.
© 2024.
23 pages.
|
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Francesco Marongiu, Domenico Santaniello.
© 2024.
25 pages.
|
|
Suchismita Satapathy.
© 2024.
19 pages.
|
|
Xinyi Gao, Minh Nguyen, Wei Qi Yan.
© 2024.
13 pages.
|
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Angelo Lorusso, Domenico Santaniello, Carmine Valentino.
© 2024.
30 pages.
|
|
Pratyay Das, Amit Kumar Shankar, Ahona Ghosh, Sriparna Saha.
© 2024.
32 pages.
|
|
|