Efficient recommendations in collaborative filtering recommender system: A multi-objective evolutionary approach based on NSGA-II algorithm

Document Type : Research Paper


1 Department of Computer Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran

2 Department of Computer Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran

3 Department of Mathematics and Computer Science, Shahed University, Tehran, Iran


The final objective of the Recommender Systems (RSs) is to offer recommendations to the user that are close to his/her taste. When the user enters the system, the most similar data cluster to the user’s taste can be selected, and by creating a neighborhood of the users similar to him/her within the selected cluster, the proposal generation can be followed. Determining the appropriate number of neighbors of the user can lead to increased accuracy of the recommendations made. Due to the enormous size of the datasets, this requires more time. This study aimed to propose recommendations with the highest accuracy and in the shortest possible time through finding the best number of neighbors for the user applying the NSGA-II Multi-Objective Evolutionary Algorithm (MOEA). Here two objects of accuracy and time of recommendations are in a multi-objective state, thus a balance should be created between the two conflict objects. The simulation results on the MovieLens 100K, MovieLens 1M, Netflix and FilmTrust standard datasets indicated that the proposed MOEA was capable of providing recommendations with greater accuracy and at the proper time, hence it could improve the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coverage, Precision, Recall and Fmeasure criteria.


Volume 14, Issue 1
January 2023
Pages 785-804
  • Receive Date: 04 August 2020
  • Revise Date: 17 August 2020
  • Accept Date: 28 September 2020