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RP-SWSGD: Design of sliding window stochastic gradient descent method with user’s ratings pattern for recommender systems

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Abstract

To offer relevant and useful recommendations, the crucial role of recommender systems in e-commerce industry is to predict the users’ concern for various items by estimating items’ attributes and users’ preferences. The reliability of a recommender system is usually assessed through accuracy and speed of relevant recommendations for a variety of items. The matrix factorization-based stochastic gradient descent (SGD) methods proposed by researchers lack memory needed to capture the ratings history hidden in the previous iterations. Recently, sliding window-based SGD strategies designed for Recommender systems and Hammerstein nonlinear systems gained attention due to the improved performance in terms of convergence speed and estimated accuracy. The memory impact with regard to the historical information enhances the performance of sliding window-based SGD techniques. However, sliding window-based methods are deficient in capturing the ratings history based on the users’ rating patterns. Hence utilizing the same window length for the set of observed ratings rated by users. Therefore, we propose an improved sliding window-based SGD strategy to acquire historical information of the ratings with respect to a user’s rating patterns for efficient matrix factorization of recommender systems. The proposed strategy performs significantly by accomplishing fast convergence speed and accuracy for window sizes greater than 1. The accuracy of the suggested technique is verified for two benchmark datasets such as ML-100 K and Film-Trust. However, the authenticity of the proposed method as compared to the standard counterpart (window size = 1) is confirmed through Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average improvements achieved by the proposed strategy in terms of RMSE and MAE over the baseline for ML-100 K dataset are 0.726% and 2.245% respectively. Whereas the proposed method accomplishes considerable average improvement of 7.89% and 9.41% for RMSE and MAE with FilmTrust dataset respectively.

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Data availability

The datasets generated during and/or analysed during the current study are available in the reference [57, 58].

Abbreviations

SGD :

Stochastic gradient descent

MF :

Matrix factorization

RS :

Recommender system

RSs :

Recommender systems

RP-SWSGD :

Rating pattern aware sliding window stochastic gradient descent

CF :

Collaborative filtering

CB :

Content-based filtering

DNN :

Deep neural networks

ALS :

Alternating least squares

RMSE :

Root mean squared error

MAE :

Mean absolute error

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, International Islamic University, Islamabad, Pakistan

    Zeshan Aslam Khan, Hafiz Anis Raja, Sumbal Iqbal & Khizer Mehmood

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, Republic of China

    Naveed Ishtiaq Chaudhary & Muhammad Asif Zahoor Raja

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  1. Zeshan Aslam Khan
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Correspondence to Naveed Ishtiaq Chaudhary.

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Khan, Z.A., Raja, H.A., Chaudhary, N.I. et al. RP-SWSGD: Design of sliding window stochastic gradient descent method with user’s ratings pattern for recommender systems. Multimed Tools Appl 83, 41083–41120 (2024). https://doi.org/10.1007/s11042-023-17258-w

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