Cross-Domain Recommendation Model Based on Fine-Grained Opinion from Review

doi:10.3778/j.issn.1002-8331.2201-0156

Abstract

Abstract: Most of the existing cross-domain recommendation（CDR） methods simply use the rating data and do not have enough information about the review. Review information contains multiple opinions of users. How to make full use of fine-grained opinion in review information to mine its potential value to better solve the problem of cross-domain recommendation cold-start and data sparsity has become the focus and difficulty of current cross-domain recommendation research. Therefore, this paper designs a cross-domain recommendation model based on fine-grained opinion from review（FGOR-CDRM）. It is mainly composed of three modules：fine-grained opinion extraction from review, auxiliary review enhancement and cross-domain correlation learning. Firstly, the text convolutional neural network is combined with the gated mechanism to guide the query by setting two global fine-grained opinion matrices to effectively extract the fine-grained opinion of the review information. Secondly, a layer of convolution is added on top of the text convolution, and auxiliary documents are constructed by using the review of similar non-overlapping users, which effectively alleviates the data sparsity while increasing the diversity of training data. Finally, the correlation between the cross-domain fine-grained opinions is learned, and the correlation matrix is constructed by using the static fine-grained opinion and the semantic matching is carried out to achieve the score prediction of the project by cold-start users in the target domain. Experiments are carried out on three domain pairs consisting of three different Amazon datasets （Book, Movies and TV, CDs and Vinyl）. The experimental results show that the performance of the FGOR-CDRM model is better than other benchmark models under the three data pairs. Taking the “movie-book” data pair as an example, the MAE of the FGOR-CDRM model is 6.09% higher than that of the ANR model in the baseline model, and 3.58% higher than that of the CDLFM model.

Key words: fine-grained opinion, cross-domain recommendation, auxiliary documents, correlation learning

摘要： 现有大多数跨域推荐（cross-domain recommendation，CDR）方法只是简单利用评分数据，对评论信息的挖掘不足。评论信息中往往包含用户的多个观点，如何充分利用评论信息中的细粒度观点挖掘其潜在价值以更好地解决跨域推荐冷启动和数据稀疏问题，成为当下跨域推荐的研究重点与难点。因此，设计了一种基于评论细粒度观点的跨域推荐模型（cross-domain recommendation model based on fine-grained opinion from review，FGOR-CDRM）。该模型主要由评论细粒度观点提取、辅助评论增强、跨域相关性学习三个模块组成。将文本卷积神经网络（text convolutional neural network，TextCNN）与门控机制结合，通过设置两个全局细粒度观点矩阵指导查询，有效提取评论信息的细粒度观点；在文本卷积之上增加一层卷积，利用相似非重叠用户的评论构建辅助文档，在增加训练数据多样性的同时有效缓解了数据稀疏；学习跨域细粒度观点之间的相关性，利用静态细粒度观点构建相关矩阵并进行语义匹配，实现目标域冷启动用户对项目的评分预测。在Amazon三个不同数据集（Book，Movies and TV，CDs and Vinyl）构成的三个领域对下进行实验，实验结果表明，FGOR-CDRM模型在三数据对下的表现均优于其他基准模型，以“电影-图书”数据对为例，FGOR-CDRM模型的（mean absolute error，MAE）比基线模型中ANR模型提高6.09%，比CDLFM模型提高3.58%。

关键词: 细粒度观点, 跨域推荐, 辅助文档, 相关性学习

WANG Yu, WU Yun. Cross-Domain Recommendation Model Based on Fine-Grained Opinion from Review[J]. Computer Engineering and Applications, 2023, 59(10): 114-122.

王禹, 吴云. 基于评论细粒度观点的跨域推荐模型[J]. 计算机工程与应用, 2023, 59(10): 114-122.

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