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.

推荐系统已经在电子商务行业中得到了详尽的应用，以向候选用户提供准确、高效和有效的个性化推荐。利用结合随机梯度下降 (SGD) 的矩阵分解 (MF) 技术的变体，通过有效处理模糊行为来提高推荐系统的效率。基于 MF 的 SGD 技术的迭代更新机制涉及用于提供相关推荐的当前但有限的信息。滑动窗口的强度和基于多创新的记忆逼近可以利用评分之间的模糊性，通过先验知识提高推荐系统的准确性。在这项工作中，