Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 330-338 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.939

Evaluation of Group Modelling Strategy in Model-Based Collaborative Filtering Recommendation

R. Mat Nawi, S. A. Mohd Noah, and L. Q. Zakaria

Abstract—Recommender systems for groups are becoming increasingly popular since many information needs instigate from group and social activities, such as listening to music, watching movies, and traveling. One of the important aspects in group recommendation is group modelling aggregation strategy which is a process to generate the overall ratings of the group. Such ratings are considered as representations of the groups. There are few group aggregations approaches. In this paper we evaluated two group aggregation approaches which are the Most Pleasure and Average strategy group modelling. We implemented both approaches on the model-based collaborative filtering technique using the single value decomposition and average least square prediction algorithms. The experimental results show that the Average strategy outperformed the Most Pleasure strategy for both prediction algorithms in terms of MAE, RMSE, and precision and recall metrics.

Index Terms—Model-based collaborative filtering, group modelling strategy, and group recommender system.

The authors are with the Center for Artificial Intelligent Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia (e-mail: rosmamalmi@siswa.ukm.edu.my, shahrul@ukm.edu.my, lailatul.qadri@ukm.edu.my). 


Cite: R. Mat Nawi, S. A. Mohd Noah, and L. Q. Zakaria, "Evaluation of Group Modelling Strategy in Model-Based Collaborative Filtering Recommendation," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 330-338, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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