MISGD: Moving-Information-Based Stochastic Gradient Descent Paradigm for Personalized Fuzzy Recommender Systems

Recommender systems have been exhaustively implemented in the e-commerce industry for providing accurate, efficient, and effective personalized recommendations to candidate users. The variants of matrix factorization (MF) techniques incorporating the stochastic gradient descent (SGD) are exploited to improve the efficacy of recommender systems through effectively dealing the fuzzy behavior. The iterative update mechanism of MF-based SGD techniques involves current but limited information for providing related recommendations. The strength of sliding window and multi-innovation-based approximations with memory can improve the accuracy of the recommender systems through prior knowledge by utilizing the fuzziness among ratings. In this work, a moving-information-based computing paradigm is presented to effectively handle the fuzzy nature of preferences by recommender systems with the ability to capture the collective effect of ratings of previous update history obtained for a defined information length to provide fast and precise recommendations.

Automated summary

Recommender systems in the e-commerce industry are optimized using matrix factorization techniques and stochastic gradient descent to improve efficacy, while also utilizing sliding windows and multi-innovation approximations to enhance accuracy.