A Temporal Recommendation Mechanism Based on Signed Network of User Interest Changes
A Temporal Recommendation Mechanism Based on Signed Network of User Interest Changes
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DOI：
10.1109/jsyst.2019.2900325
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发表时间：
2020-03
影响因子：
4.4
通讯作者：
Jianrui Chen;Lidan Wei;Liji U;Fei Hao
中科院分区：
文献类型：
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作者：
Jianrui Chen;Lidan Wei;Liji U;Fei Hao
在推荐系统中，一个关键的任务就是帮助用户在大量的项目中找到自己感兴趣的项目。推荐方法和技术的发展推动了许多现实世界的推荐应用。合意推荐对目标用户和推荐项目都至关重要。从不同的时间跨度挖掘有意义的兴趣信息是推荐精度的关键。本文利用不同时间跨度之间的关系构造签名网络，模拟用户兴趣变化。首先，我们最初利用时间分数信息来划分不同的时间跨度。然后，根据用户兴趣的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点，我们定义了一个新的相似性度量，以把握两个相邻的签名网络之间的共同兴趣。通过对不同时间步长的网络邻接矩阵进行加权，得到一个新的邻接矩阵。根据所构建的动态演化聚类模型，将节点划分为不同的簇。最后，在每个子类中计算预测评分，而不是整个系统，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，进行了广泛的模拟，证明使用我们的计划，推荐的准确性显着提高。
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.