Graph-Based Collaborative Filtering with MLP
Graph-Based Collaborative Filtering with MLP
复制标题
使用 MLP 的基于图的协同过滤
DOI：
10.1155/2018/8314105
复制
发表时间：
2018-01-01
影响因子：
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通讯作者：
Hong, Qingqi
中科院分区：
文献类型：
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作者：
Lu, Shengyu;Chen, Hangping;Hong, Qingqi
协同过滤方法在推荐系统中得到了广泛的应用。他们从用户的历史数据中了解用户的兴趣和偏好，然后推荐用户可能喜欢的产品。然而，现有的方法通常通过计算相关系数来度量用户之间的相关性，无法捕捉到用户之间的潜在特征。本文提出了一种基于图的算法。首先将用户信息转化为向量，利用奇异值分解方法降维，然后根据改进的核函数学习到所有用户的偏好和兴趣，并映射到网络中；最后，我们通过多层感知器（Multilayer Perceptron， MLP）预测用户对物品的评分。与现有的方法相比，我们的方法一方面可以通过将用户信息映射到网络中来发现用户之间的一些潜在特征；另一方面，我们将带有评分信息的向量改进为MLP方法，并对物品的评分进行预测，从而达到更好的推荐效果。
The collaborative filtering (CF) methods are widely used in the recommendation systems. They learn users' interests and preferences from their historical data and then recommend the items users may like. However, the existing methods usually measure the correlation between users by calculating the coefficient of correlation, which cannot capture any latent features between users. In this paper, we proposed an algorithm based on graph. First, we transform the users' information into vectors and use SVD method to reduce dimensions and then learn the preferences and interests of all users based on the improved kernel function and map them to the network; finally, we predict the user's rating for the items through the Multilayer Perceptron (MLP). Compared with existing methods, on one hand, our method can discover some latent features between users by mapping users' information to the network. On the other hand, we improve the vectors with the ratings information to the MLP method and predict the ratings for items, so we can achieve better effects for recommendation.