In recommender systems, one of critical tasks is to help users find their interested items among huge amount of items. The development of recommendation methods and techniques has driven many real-world recommendation applications. Desirable recommendations are vital both to the target users and the recommended items. Mining meaningful interest information from different time spans is crucial for recommendation precision. In this paper, the relations between different time spans are adopted to construct the signed networks and imitate users interest changes. First, we initially utilize temporal score information to divide different time spans. Then, the signed networks are formed at different time steps referring to users interest changes. Furthermore, considering the characteristics of signed networks, we define a new similarity measurement to grasp the common interest between two adjacent signed networks. A new adjacent matrix is obtained by weighting the network adjacent matrices of different time steps. According to the constructed dynamic evolutionary clustering model in a signed network, the nodes are divided into different clusters. Finally, the predicted ratings are calculated in each subclass instead of the entire system, which reduces the computational cost greatly. The extensive simulations are conducted based on two real-world datasets, Movielens and CiaoDVD, for demonstrating that the recommend accuracy is significantly improved using our scheme.

中文翻译：

---

基于用户兴趣变化签名网络的时间推荐机制

在推荐系统中，一项关键任务是帮助用户在大量项目中找到他们感兴趣的项目。推荐方法和技术的发展推动了许多现实世界中的推荐应用程序。理想的推荐对目标用户和推荐项目都至关重要。从不同的时间跨度中获取有意义的兴趣信息对于推荐精度至关重要。本文采用不同时间跨度之间的关系来构建签名网络并模拟用户的兴趣变化。首先，我们首先利用时间得分信息来划分不同的时间跨度。然后，参考用户兴趣变化在不同的时间步形成签名网络。此外，考虑到签名网络的特征，我们定义了一种新的相似性度量，以掌握两个相邻签名网络之间的共同兴趣。通过加权不同时间步长的网络相邻矩阵，可以得到一个新的相邻矩阵。根据签名网络中构建的动态进化聚类模型，将节点分为不同的聚类。最后，在每个子类中而不是整个系统中计算预测等级，这大大降低了计算成本。基于两个真实世界的数据集Movielens和CiaoDVD进行了广泛的仿真，以证明使用我们的方案可以大大提高推荐的准确性。根据签名网络中构建的动态进化聚类模型，将节点分为不同的聚类。最后，在每个子类中而不是整个系统中计算预测等级，这大大降低了计算成本。基于两个真实世界的数据集Movielens和CiaoDVD进行了广泛的仿真，以证明使用我们的方案可以大大提高推荐的准确性。根据签名网络中构建的动态进化聚类模型，将节点分为不同的聚类。最后，在每个子类中而不是整个系统中计算预测等级，这大大降低了计算成本。基于两个真实世界的数据集Movielens和CiaoDVD进行了广泛的仿真，以证明使用我们的方案可以大大提高推荐的准确性。