Collaborative Filtering Technical Comparison in Implicit Data

Ali Kourtiche and Mohamed Merabet
Additional contact information
Ali Kourtiche: EEDIS Laboratory, Computer Science Department, University of Sidi Bel Abbes, Algeria
Mohamed Merabet: EEDIS Laboratory, Computer Science Department, University of Sidi Bel Abbes, Algeria

International Journal of Knowledge-Based Organizations (IJKBO), 2021, vol. 11, issue 4, 1-24

Abstract: Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set benchmark is crucial for the proper evaluation of collaborative filtering algorithms in order to draw a conclusion on the performance of the algorithms.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJKBO.2021100101 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jkbo00:v:11:y:2021:i:4:p:1-24

Access Statistics for this article

International Journal of Knowledge-Based Organizations (IJKBO) is currently edited by John Wang

More articles in International Journal of Knowledge-Based Organizations (IJKBO) from IGI Global
Bibliographic data for series maintained by Journal Editor ().