融合上下文信息的深度推荐模型

目前,在基于文档信息的推荐任务中,传统基于文档的混合推荐算法仍依赖于浅层的线性模型,当评分数据变得庞大且复杂时,其推荐性能往往不太理想。针对此问题,提出一种深度融合模型(DeepFM),该模型能够在完全捕获文本信息的同时也能很好地处理复杂且稀疏的评分数据。DeepFM由两个并行的神经网络组成,其中一路神经网络使用多层感知器提取评分矩阵的行向量信息从而获得用户的潜在特征向量,另一路则使用MLP和卷积神经网络(CNN)共同建模从而提取额外有关项目的文本信息得到项目潜在特征向量。最后,通过构建融合层将用户特征向量和项目特征向量进行融合得出预测评分。实验结果表明,DeepFM在MovieLens数据集和亚马逊数据集上的性能优于主流的推荐模型。

Deep recommendation model with context information

At present,in the recommendation task based on document information,the traditional document-based hybrid recom-mendation algorithm still relies on a shallow linear model.When the scoring data becomes huge and complex,its recommendation performance is often not ideal.This paper proposed a deep fusion model(DeepFM),which could fully capture text information and handle complex and sparse scoring data well.DeepFM consisted of two parallel neural networks.One of them used multiple layers of perceptron to extract the row vector information of the rating matrix to obtain the user’s potential feature vector.The other one used fusion model of MLP and convolutional neural network(CNN)to extract additional textual information about the item and obtain the item’s potential feature vector.Finally,it fused the user feature vector and the item feature vector by constructing a fusion layer to obtain a prediction score.Experimental results show that DeepFM outperforms mainstream recommendation models on MovieLens dataset and Amazon dataset.