Efficient recommendations in collaborative filtering recommender system: A multi-objective evolutionary approach based on NSGA-II algorithm
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 108
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شناسه ملی سند علمی:
JR_IJNAA-14-1_060
تاریخ نمایه سازی: 5 شهریور 1402
چکیده مقاله:
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 ۱۰۰K, MovieLens ۱M, 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.
کلیدواژه ها:
نویسندگان
Touraj Mohammadpour
Department of Computer Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran
Amir Massoud Bidgoli
Department of Computer Engineering, Tehran North Branch, Islamic Azad University, Tehran, Iran
Rasul Enayatifar
Department of Computer Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
Hamid Haj Seyyed Javadi
Department of Mathematics and Computer Science, Shahed University, Tehran, Iran