基于融合卷积神经网络的协同过滤模型

Collaborative Filtering Model Based on Fusion Convolutional Neural Network

在传统推荐系统中,通常基于协同过滤推荐算法挖掘评分中的隐含特征,但在实际应用中,评分矩阵往往十分稀疏,很难充分地表示用户偏好特征与物品描述特征。为了充分挖掘评论文本中的隐含特征,并在一定程度上缓解数据稀疏性问题,提出一种深度协同过滤模型(CFiCNN):融合卷积神经网络的协同过滤模型。该模型利用卷积神经网络抽取用户-物品评论数据中的隐含特征,基于协同过滤的矩阵分解方法,进行评分预测。在4个真实数据集上对该模型进行了评估实验,并与3个常用模型进行对比。实验结果表明,该模型能够很好地抽取到用户与物品的隐含特征,并且进行更准确的评分预测。

In the traditional recommendation system,collaborative filtering is widely used to extract implied features.However,in practice,the rating matrix is usually very sparse,which is difficult to fully represent users＇ preferences and items＇ features.In order to fully extract the implicit features in reviews and to alleviate the sparseness of data to a certain extent,this paper proposes a deep collaborative filtering model,named collaborative filtering integrating convolutional neural network（CFiCNN）.This model uses convolution neural network to extract the implicit features in the user-item reviews,and then predicts ratings based on the matrix factorization.In this paper,CFiCNN is evaluated on four real data sets and compared with three commonly used models.The experimental results show that CFiCNN can extract the implicit features of the user and the commodity well,and make a more accurate rating prediction.