Research on Recommendation Based on DeepFM and Graph Embedding
首发时间:2020-03-17
Abstract:The traditional recommendation system usually focuses on the coupling of feature information between users and items, but fails to effectively investigate the complex networks of users and items. At the same time, the graph algorithms are often used to analyze the point-edge relationships in networks, and we can combine as many network node features as possible through graph machine learning. To this end, in this paper, by combining the graph algorithm with the recommendation algorithm, prediction is conducted by embedding information. First, we employ the DeepFM and the GNNs to perform information mining of explicit and implicit features of the feature information and the heterogeneous structure network. Then, we combine the features of the two embedding layers to construct the final embedding vector. Finally, we use a multi-layer fully connected and activation function to predict the results. Two standard data sets are used in our experiment. The results show that the new model has the best performance in the recommended field.
keywords: Recommendation System Graph Embedding DeepFM
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基于深度推荐网络和图卷积网络的推荐算法
摘要:传统的推荐系统通常侧重于用户和物品之间的特征信息的耦合,但是不能有效地探索用户和物品的复杂网络。同时,图算法通常用于挖掘网络中的点-边关系,并且我们可以通过图神经网络来组合尽可能多的网络节点特征。为此,在本文中,通过将图算法与推荐算法相结合,使用嵌入信息来进行预测。首先,我们使用DeepFM和图神经网络进行特征信息和异构网络的显式和隐式特征的信息挖掘。然后,我们结合两个嵌入特征向量来构建最终的预测特征向量。最后,我们使用全连接层和激活函数完成结果的预测。论文实验中使用了两个标准数据集。结果表明,新模型在推荐结果中具有最佳性能。
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