In the past, making decisions or recommendations, as well as processing data, did not pose significant challenges due to the limited data related to a small number of users. However, with the continuous growth of the population and the exponential increase in data and user profiles across global databases, the task of generating improved decisions or recommendations in terms of time, location, cost, and other characteristics has become more complex. Recommender systems, data mining techniques, and algorithms play a crucial role in addressing these challenges. The escalating attention of both researchers and practitioners towards recommender and data mining systems reflects the increasing difficulty in handling vast datasets efficiently. This article aims to analyze a relatively extensive dataset with diverse characteristics, seeking to achieve optimal clustering or categorization and regression in the shortest possible time, considering economic efficiency and other key features inherent to the dataset. The dataset under consideration exhibits data oscillation in four features. Initially, clustering and regression are performed using the implicit method. Additionally, employing the collaborative filtering approach based on recommender systems, specifically the collaborative filtering method using the ranking matrix (user-item collaboration), is employed. This method yields highly effective recommendations for new users based on a variety of essential criteria.
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FazlAazad,M. (2024). Performance Improvement for Multi-Criteria Decision Making Using Collaborative Filtering-Based Recommender Systems. Transactions on Machine Intelligence, 7(1), 51-60. doi: 10.47176/TMI.2024.51
MLA
FazlAazad,M. . "Performance Improvement for Multi-Criteria Decision Making Using Collaborative Filtering-Based Recommender Systems", Transactions on Machine Intelligence, 7, 1, 2024, 51-60. doi: 10.47176/TMI.2024.51
HARVARD
FazlAazad M. (2024). 'Performance Improvement for Multi-Criteria Decision Making Using Collaborative Filtering-Based Recommender Systems', Transactions on Machine Intelligence, 7(1), pp. 51-60. doi: 10.47176/TMI.2024.51
CHICAGO
M. FazlAazad, "Performance Improvement for Multi-Criteria Decision Making Using Collaborative Filtering-Based Recommender Systems," Transactions on Machine Intelligence, 7 1 (2024): 51-60, doi: 10.47176/TMI.2024.51
VANCOUVER
FazlAazad M. Performance Improvement for Multi-Criteria Decision Making Using Collaborative Filtering-Based Recommender Systems. Trans. Mach. Intell., 2024; 7(1): 51-60. doi: 10.47176/TMI.2024.51
