Parallel Fractional Stochastic Gradient Descent with Adaptive Learning for Recommender Systems

The structural change toward the digital transformation of online sales elevates the importance of parallel processing techniques in recommender systems, particularly in the pandemic and post-pandemic era. Matrix factorization (MF) is a popular and scalable approach in collaborative filtering (CF) t...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2024-03, Vol.35 (3), p.1-14
Main Authors: Elahi, Fatemeh, Fazlali, Mahmood, Malazi, Hadi Tabatabaee, Elahi, Mehdi
Format: Article
Language:English
Subjects:
Accuracy
Big Data
Collaborative filtering
Convergence
Fractional calculus
Graphics processing units
Learning
Optimization techniques
Pandemics
Parallel Matrix Factorization
Parallel processing
Performance evaluation
Recommender systems
Sparse matrices
Stochastic processes
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Summary:The structural change toward the digital transformation of online sales elevates the importance of parallel processing techniques in recommender systems, particularly in the pandemic and post-pandemic era. Matrix factorization (MF) is a popular and scalable approach in collaborative filtering (CF) to predict user preferences in recommender systems. Researchers apply Stochastic Gradient Descent (SGD) as one of the most famous optimization techniques for MF. Paralleling SGD methods help address Big Data challenges due to the wide range of products and the sparsity in user ratings. However, these methods' convergence rate and accuracy are affected by the dependency between the user and item latent factors, specifically in large-scale problems. Besides, the performance is sensitive to the applied learning rates. This paper proposes a new parallel method to remove dependencies and boost speed-up by using fractional calculus to improve accuracy and convergence rate. We also apply adaptive learning rates to enhance the performance of our proposed method. The proposed method is based on Compute Unified Device Architecture (CUDA) platform. We evaluate the performance of our proposed method using real-world data and compare the results with the close baselines. The results show that our method can obtain high accuracy and convergence rate in addition to high parallelism.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2022.3185212