Recommendation systems play more and more important roles in many fields, such as movie recommendation, book recommendation, music recommendation. Sparse data and large-scale data result in low recommendation efficiency and a cold start problem. Recently, matrix decomposition plays an active role in recommendation with its high recommendation efficiency and fast recommendation speed. But in most matrix completion mechanisms, time influence and users features are not fully utilized. In this paper, a continuous function is constructed to grasp the time-varying rule of users’ interests, and the scores of different scoring time are processed by this function. Thus, the processed scores contain not only the user interests but also their memory rule. Moreover, user interests may change slowly over time, so we keep the original rating matrix in the recommendation process. Finally, a new matrix decomposition optimization model is constructed by considering the score matrix that combines time information and the original score matrix. Compared with the recent matrix decomposition recommendation algorithms, the effectiveness of our proposed algorithm is verified based on the recommended evaluation metrics and several datasets.

推荐系统在电影推荐，书籍推荐，音乐推荐等许多领域中起着越来越重要的作用。稀疏数据和大规模数据导致推荐效率低和冷启动问题。近年来，矩阵分解以其高推荐效率和快速推荐速度在推荐中起着积极作用。但是在大多数矩阵完成机制中，时间影响和用户功能没有得到充分利用。本文构造了一个连续函数来把握用户兴趣的时变规律，并利用该函数处理不同计分时间的得分。因此，处理后的分数不仅包含用户兴趣，还包含他们的记忆规则。此外，用户的兴趣可能会随着时间的推移而缓慢变化，因此我们会在推荐过程中保留原始评分矩阵。最后，通过考虑结合了时间信息的分数矩阵和原始分数矩阵，构造了一个新的矩阵分解优化模型。与最近的矩阵分解推荐算法相比，我们基于推荐的评估指标和几个数据集验证了该算法的有效性。