The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2020.0111083
PDF

Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering

Author 1: Edward Hinojosa-Cardenas
Author 2: Edgar Sarmiento-Calisaya
Author 3: Cesar A. Martinez-Salinas
Author 4: Lehi Quincho-Mamani
Author 5: Jair F. Huaman-Canqui

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 10, 2020.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: In the era of internet, several online platforms offer many items to users. Users could spend a lot of time to find (or not) some items they are interested, sometimes, they will probably not find the desired items. An effective strategy to overcome this problem is a recommender system, one of the most popular applications of machine learning. Recommender systems select most appropriate items to an specific user based on previous information between items and users, and they are developed using diffeent approaches. One of the most successful approach for developing recommender systems is collaborative filtering, which can filter out items that a user might like based on reactions of users with similar profiles. Often, traditional recommender systems only consider precision as evaluation metric of performance, however, others metrics (like recall, diversity, novelty, etc) are also important. Unfortunately, some metrics are conflicting, e.g., precision impacts negatively on other metrics. This paper presents a multi-objective evolutionary programming method for developing a recommender system, which is based on a new collaborative filtering technique, while maximizes the recall for a given precision, The new collaborative filtering technique uses three components for recommending an item to a user: 1) clustering of users; 2) a previous memory-based prediction; and 3) five decimal parameters (threshold average clustering, threshold penalty, threshold incentive, weight attached to average clustering and weight attached to Pearson correlation). The multiobjective evolutionary programming optimizes the clustering of users and the five decimal parameters, while, it searches maximizes both similarity precision and recall objectives. A comparison between the proposed method and a previous nonevolutionary method shows that the proposed method improves precision and recall metric on a benchmark database.

Keywords: Collaborative filtering; clustering; evolutionary programming; multi-objective; recommender systems

Edward Hinojosa-Cardenas, Edgar Sarmiento-Calisaya, Cesar A. Martinez-Salinas, Lehi Quincho-Mamani and Jair F. Huaman-Canqui, “Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering” International Journal of Advanced Computer Science and Applications(IJACSA), 11(10), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111083

@article{Hinojosa-Cardenas2020,
title = {Multi-Objective Evolutionary Programming for Developing Recommender Systems based on Collaborative Filtering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111083},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111083},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {10},
author = {Edward Hinojosa-Cardenas and Edgar Sarmiento-Calisaya and Cesar A. Martinez-Salinas and Lehi Quincho-Mamani and Jair F. Huaman-Canqui}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org