基于耦合CNN评分预测模型的个性化商品推荐

电子商务中大量评论数据蕴含着丰富的信息,该信息有助于解决个性化推荐系统存在的数据稀疏问题.为了充分挖掘评论数据蕴含的价值,提高商品推荐的准确率,本文提出了基于耦合CNN评分预测模型的个性化商品推荐方法.该方法首先利用耦合CNN构建评分预测模型,将耦合CNN分为用户网络和商品网络,划分成输入层、卷积层、输出层和共享层;用户评论数据和商品评论数据分别从相应网络输入;在评论数据分析时,从字向量角度进行语义分析,同时改变传统的使用单一大小卷积核处理句子的模式,使用多个并行的卷积层,利用大小不同的卷积核对句子进行特征提取;两个网络的输出将共同汇聚于共享层,在共享层使用因子分解机进行评分预测;最后将结果中的高评分商品推荐给用户.经对比实验验证,本文所给方法能够提高商品推荐的准确率.

Personalized Commodity Recommendation Based on Coupled CNN Predictive Scoring Model

A large number of comment data in e-commerce contain abundant information,the information helps to solve the problem of data sparsity in personalized recommendation system. In order to improve the efficiency of using comment data and the accuracy of commodity recommendation,a personalized commodity recommendation method based on coupled CNN scoring predictive model was proposed. This method uses CNN to construct scoring prediction model and divides the coupled CNN into user network and commodity network,which are divided into input layer,convolution layer,output layer and sharing layer. User comment and commodity comment are input from corresponding network respectively. In the analysis of commentary data,semantic analysis is carried out from the perspective of word vector,while changing the traditional sentence processing mode using single-size convolution kernel,using multiple parallel convolution layers,using multiple convolution kernels with different sizes to extract sentence features. The outputs of the two networks are converged in the sharing layer,where the Factorization Machine algorithm is used for scoring prediction. Finally,the highscore commodities in the results are recommended to users. The results of comparative experiments show that the proposed method can improve the accuracy of commodity recommendation.