Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Improving Collaborative Filtering-based recommender system Using new hybrid meta-heuristic algorithm

Document Type : Persian Original Article

Authors
1 Ph.D. Candidate, Department of Information Technology Management, SR.C., Islamic Azad University, Tehran, Iran.
2 Associate Professor, Department of Industrial Management and Technology, SR.C., Islamic Azad University, Tehran, Iran.
3 Professor, Department of Industrial Management and Technology, SR.C., Islamic Azad University, Tehran, Iran.
Abstract
Abstract- Collaborative filtering (CF) is the most widely used recommendation technique because it relies on user interactions rather than specific user or item features. The neighborhood-based (memory-based) CF method predicts user preferences by measuring similarities between users or items and similarity weights have a noticeable effect on recommendation quality. Therefore in this study, the similarity calculation between users is formulated as an optimization problem, and a novel hybrid metaheuristic algorithm is proposed to improve its weights. This algorithm combines Grey Wolf Optimizer (GWO), Lion Optimization Algorithm (LOA), and Differential Evolution (DE) to determine the optimal similarity weights. The performance of the proposed algorithm is evaluated using Mean Absolute Error (MAE), Precision, Recall, and F-measure. The average MAE for the proposed algorithm, GWO and PSO are 0.4919, 0.5544, and 0.5578 respectively, indicating a significant reduction in MAE, and an improvement in prediction quality compared to baseline algorithms. Also, the evaluations using 175 nearest neighbors and varying numbers of recommendations demonstrate improvements in Precision, Recall and F-measure, reflecting enhanced recommendation quality. Finally, the proposed algorithm was assessed on a set of standard benchmark functions and compared with GWO and PSO algorithms, showing superior performance in minimizing.
Keywords

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