Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems

Elahi, Fatemeh; Fazlali, Mahmood; Malazi, Hadi Tabatabaee; Elahi, Mehdi

doi:10.1109/tpds.2022.3185212

Cited by 7 publications

(4 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We reimplement the SGD-MF, Fractional-MF, and hybrid-MF (FASGD-MF) based on the proposed methods in [25], and [24]. Moreover, we used the result of experiments in parameter setting and applied the genetic algorithm to the aforementioned methods, named GSGD-MF, GFractional-MF, and GFASGD-MF.…”

Section: Implementation Resultsmentioning

confidence: 99%

“…where α f is the fractional learning rate and, f r is the fractional order of the function. In the hybrid method (FASGD-MF), a weighted combination of two previous methods is considered [24]. The formulas to update P and Q matrices are changed as follows:…”

Section: B Fractional Adaptive Stochastic Gradient Descent Approachmentioning

confidence: 99%

“…The main reason for the primacy of this method over the other methods is that FASGD is utilized for factorizing the rating matrix, which fundamentally has a high accuracy and convergence rate. It is discussed in more detail in [24].…”

Section: E Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Auto-tuning HyperParameters of SGD Matrix Factorization-Based Recommender Systems Using Genetic Algorithm

Irani

Elahi

Fazlali

et al. 2022

2022 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)

View full text Add to dashboard Cite

Recommender systems enable companies to generate meaningful recommendations to users for items or products that might interest them. Stochastic Gradient Descent Matrix Factorization (SGD-MF) is one of the most popular modelbased recommender systems. Fractional Adaptive Stochastic Gradient Descent matrix factorization (FASGD-MF) is a subset of SGD-MF-based models that apply fractional calculus in an adaptive way. There are some hyperparameters in these models that impact the quality of the recommender system. However, searching the hyperparameter space to find the best configuration using an exhaustive search is often a time-consuming task. This paper employs a genetic algorithm as a search metaheuristic to tackle this problem. The proposed method is designed based on non-uniform mutation and whole arithmetic crossover. The results indicate that optimizing hyperparameters by the proposed method not only adjusts the values of hyperparameters automatically but also can improve the quality of SGD-MF-based models. Implementing the proposed genetic algorithm on two datasets (MovieLens 100K and MovieLens 1M) verifies the assertion about the performance.

show abstract

Section: Implementation Resultsmentioning

confidence: 99%

Section: B Fractional Adaptive Stochastic Gradient Descent Approachmentioning

confidence: 99%

See 1 more Smart Citation

Auto-tuning HyperParameters of SGD Matrix Factorization-Based Recommender Systems Using Genetic Algorithm

Irani

Elahi

Fazlali

et al. 2022

2022 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)

View full text Add to dashboard Cite

show abstract

“…Image classification, speech recognition, natural language processing Federated Stochastic Gradient Descent (FSGD) [15] Aggregation of stochastic gradients from all participating devices to update the global model Healthcare, finance, edge computing Federated Learning with Secure Aggregation (FSA) [16] Encrypted data and model parameters are transferred from participating devices to a central server, where the aggregation is performed with the help of secure multiparty computation (MPC)…”

Section: Aggregation Methods Description Use Casesmentioning

confidence: 99%

Enhancing Privacy-Preserving Intrusion Detection through Federated Learning

Alazab

Khraisat²,

Singh

et al. 2023

Electronics

View full text Add to dashboard Cite

Detecting anomalies, intrusions, and security threats in the network (including Internet of Things) traffic necessitates the processing of large volumes of sensitive data, which raises concerns about privacy and security. Federated learning, a distributed machine learning approach, enables multiple parties to collaboratively train a shared model while preserving data decentralization and privacy. In a federated learning environment, instead of training and evaluating the model on a single machine, each client learns a local model with the same structure but is trained on different local datasets. These local models are then communicated to an aggregation server that employs federated averaging to aggregate them and produce an optimized global model. This approach offers significant benefits for developing efficient and effective intrusion detection system (IDS) solutions. In this research, we investigated the effectiveness of federated learning for IDSs and compared it with that of traditional deep learning models. Our findings demonstrate that federated learning, by utilizing random client selection, achieved higher accuracy and lower loss compared to deep learning, particularly in scenarios emphasizing data privacy and security. Our experiments highlight the capability of federated learning to create global models without sharing sensitive data, thereby mitigating the risks associated with data breaches or leakage. The results suggest that federated averaging in federated learning has the potential to revolutionize the development of IDS solutions, thus making them more secure, efficient, and effective.

show abstract

Stochastic Gradient Descent for matrix completion: Hybrid parallelization on shared- and distributed-memory systems

Büyükkaya,

Karsavuran,

Aykanat

2024

Knowledge-Based Systems

View full text Add to dashboard Cite

Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems

Cited by 7 publications

References 40 publications

Auto-tuning HyperParameters of SGD Matrix Factorization-Based Recommender Systems Using Genetic Algorithm

Auto-tuning HyperParameters of SGD Matrix Factorization-Based Recommender Systems Using Genetic Algorithm

Enhancing Privacy-Preserving Intrusion Detection through Federated Learning

Stochastic Gradient Descent for matrix completion: Hybrid parallelization on shared- and distributed-memory systems

Contact Info

Product

Resources

About