With the development of e-commerce, various methodologies have studied to improve recommendation performance. Recently, many deep learning based network embedding approaches are applied to the recommendation domain. However, these approaches still have several limitations, such as the problem of data sparseness and the changing in user preference over time, which cannot be considered. In this paper, we propose a novel method for item recommendation based on network embedding. First, we apply a bipartite network embedding to address the data sparsity problem. Bipartite network embedding is a vector representation method that reflects explicit (i.e., observed data) and implicit relations (i.e., unobserved data). Bipartite network embedding methodology can address the data sparsity problem by using implicit relationship information from applying the random walk approach. Second, we predict future bipartite network embedding of user preference by adopting a Kalman filter to consider the changes in user preferences. We have conducted experiments to evaluate the effectiveness and performance of the proposed recommendation method. Through experimentation, the proposed recommendation method is validated as outperforming than the existing approaches including existing network embedding methods.

随着电子商务的发展，已经研究了各种方法来改善推荐性能。近来，许多基于深度学习的网络嵌入方法被应用于推荐域。但是，这些方法仍然存在一些局限性，例如数据稀疏性问题和用户偏好随时间的变化，这是无法考虑的。本文提出了一种基于网络嵌入的商品推荐新方法。首先，我们应用双向网络嵌入来解决数据稀疏性问题。双向网络嵌入是一种向量表示方法，可反映显式（即，观察到的数据）和隐式关系（即，未观察到的数据）。双向网络嵌入方法可以通过应用来自随机游走方法的隐式关系信息来解决数据稀疏性问题。其次，我们通过采用卡尔曼滤波器来考虑用户偏好的变化，来预测用户偏好的未来两方网络嵌入。我们进行了实验，以评估所提出的推荐方法的有效性和性能。通过实验，验证了所推荐的推荐方法优于包括现有网络嵌入方法在内的现有方法。我们进行了实验，以评估所提出的推荐方法的有效性和性能。通过实验，验证了所推荐的推荐方法优于包括现有网络嵌入方法在内的现有方法。我们进行了实验，以评估所提出推荐方法的有效性和性能。通过实验，验证了所推荐的推荐方法优于包括现有网络嵌入方法在内的现有方法。