Increasing Performance of Recommender Systems by Combining Deep Learning and Extreme Learning Machine
- سال انتشار: 1401
- محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 10، شماره: 2
- کد COI اختصاصی: JR_JADM-10-2_004
- زبان مقاله: انگلیسی
- تعداد مشاهده: 306
نویسندگان
Computer Engineering Department, Shomal University, Amol, Iran.
Computer Engineering Department, Shomal University, Amol, Iran
Computer Engineering Department, Shomal University, Amol, Iran.
چکیده
Nowadays, with the expansion of the internet and its associated technologies, recommender systems have become increasingly common. In this work, the main purpose is to apply new deep learning-based clustering methods to overcome the data sparsity problem and increment the efficiency of recommender systems based on precision, accuracy, F-measure, and recall. Within the suggested model of this research, the hidden biases and input weights values of the extreme learning machine algorithm are produced by the Restricted Boltzmann Machine and then clustering is performed. Also, this study employs the ELM for two approaches, clustering of training data and determine the clusters of test data. The results of the proposed method evaluated in two prediction methods by employing average and Pearson Correlation Coefficient in the MovieLens dataset. Considering the outcomes, it can be clearly said that the suggested method can overcome the problem of data sparsity and achieve higher performance in recommender systems. The results of evaluation of the proposed approach indicate a higher rate of all evaluation metrics while using the average method results in rates of precision, accuracy, recall, and F-Measure come to ۸۰.۴۹, ۸۳.۲۰, ۶۷.۸۴ and ۷۳.۶۲ respectively.کلیدواژه ها
Recommender Systems, Extreme learning machine, Restricted Boltzmann Machine, Data sparsity, Clustering methodsاطلاعات بیشتر در مورد COI
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