一种基于文本卷积神经网络的推荐算法

传统的矩阵因子分解模型不能有效提取用户和物品特征，而基于深度学习模型可以很好地提取特征信息。当前，主流的基于深度学习推荐算法只是单一地将神经网络的输出或物品特征与用户特征乘积的形式来做推荐预测，不能充分挖掘用户和物品之间的关系。基于此，本文提出一种基于文本卷积神经网络与带偏置项的奇异值分解(BiasSVD)结合的推荐算法，利用文本卷积神经网络(TextCNN)来充分提取用户和物品的特征信息，然后用奇异值分解方法来做推荐，深层次理解文档上下文信息，进一步提高推荐的准确性。将该算法在MovieLens的2个真实数据集上做广泛的评估分析，推荐的准确度要明显优于ConvMF算法及主流深度学习推荐算法。

A Recommendation Algorithm Based on Text Convolutional Neural Network

The traditional matrix factorization model can not effectively extract the features of users and items, while the deep learning model can extract the feature information well. At present, the mainstream recommendation algorithm based on deep learning only uses the output of neural network or the product of item features and user features to make recommendation prediction, which can not fully mine the relationship between users and items. Based on this, this paper proposes a recommendation algorithm based on the combination of text convolutional neural network and bias singular value decomposition (BiasSVD). Text convolutional neural network (TextCNN) is used to fully extract the feature information of users and items, and then singular value decomposition method is used to make recommendations, which can deeply understand the document context information and further improve the accuracy of recommendation. After extensive evaluation and analysis on two real datasets of MovieLens, the recommendation accuracy of this algorithm is obviously better than that of ConvMF algorithm and mainstream deep learning recommendation algorithm.