Recommendation systems suggest peculiar products to customers based on their past ratings, preferences, and interests. These systems typically utilize collaborative filtering (CF) to analyze customers’ ratings for products within the rating matrix. CF suffers from the sparsity problem because a large number of rating grades are not accurately determined. Various prediction approaches have been used to solve this problem by learning its latent and temporal factors. A few other challenges such as latent feedback learning, customers’ drifting interests, overfitting, and the popularity decay of products over time have also been addressed. Existing works have typically deployed either short or long temporal representation for addressing the recommendation system issues. Although each effort improves on the accuracy of its respective benchmark, an integrative solution that could address all the problems without trading off its accuracy is needed. Thus, this paper presents a Latent-based Temporal Optimization (LTO) approach to improve the prediction accuracy of CF by learning the past attitudes of users and their interests over time. Experimental results show that the LTO approach efficiently improves the prediction accuracy of CF compared to the benchmark schemes.

中文翻译：

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基于潜伏的时间优化方法，以提高协同过滤的性能

推荐系统根据客户过去的评分，偏好和兴趣向他们建议特殊的产品。这些系统通常利用协作过滤（CF）来分析客户对评级矩阵中产品的评级。CF存在稀疏性问题，因为无法准确确定大量的评级等级。通过学习潜在和时间因素，各种预测方法已用于解决该问题。还解决了其他一些挑战，例如潜在的反馈学习，客户的兴趣漂移，过度拟合以及产品随时间推移的受欢迎程度下降。现有的工作通常已经部署了短期或长期的时间表示，以解决推荐系统问题。尽管每项工作都会提高各自基准的准确性，需要一种能够解决所有问题而不牺牲其准确性的集成解决方案。因此，本文提出了一种基于潜伏的时间优化（LTO）方法，通过学习用户过去的态度及其兴趣随时间的变化来提高CF的预测准确性。实验结果表明，与基准方案相比，LTO方法有效地提高了CF的预测精度。