Wang Yongpeng, Yu Hong, Wang Guoyin, Xie Yongfang
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Entropy (Basel). 2020 Apr 20;22(4):473. doi: 10.3390/e22040473.
Cross-domain recommendation is a promising solution in recommendation systems by using relatively rich information from the source domain to improve the recommendation accuracy of the target domain. Most of the existing methods consider the rating information of users in different domains, the label information of users and items and the review information of users on items. However, they do not effectively use the latent sentiment information to find the accurate mapping of latent features in reviews between domains. User reviews usually include user's subjective views, which can reflect the user's preferences and sentiment tendencies to various attributes of the items. Therefore, in order to solve the cold-start problem in the recommendation process, this paper proposes a cross-domain recommendation algorithm (CDR-SAFM) based on sentiment analysis and latent feature mapping by combining the sentiment information implicit in user reviews in different domains. Different from previous sentiment research, this paper divides sentiment into three categories based on three-way decision ideas-namely, positive, negative and neutral-by conducting sentiment analysis on user review information. Furthermore, the Latent Dirichlet Allocation (LDA) is used to model the user's semantic orientation to generate the latent sentiment review features. Moreover, the Multilayer Perceptron (MLP) is used to obtain the cross domain non-linear mapping function to transfer the user's sentiment review features. Finally, this paper proves the effectiveness of the proposed CDR-SAFM framework by comparing it with existing recommendation algorithms in a cross-domain scenario on the Amazon dataset.
跨域推荐是推荐系统中一种很有前景的解决方案，它通过使用源域中相对丰富的信息来提高目标域的推荐准确性。现有的大多数方法都考虑了不同域中用户的评分信息、用户和物品的标签信息以及用户对物品的评论信息。然而，它们没有有效地利用潜在的情感信息来找到不同域评论中潜在特征的准确映射。用户评论通常包含用户的主观观点，这些观点可以反映用户对物品各种属性的偏好和情感倾向。因此，为了解决推荐过程中的冷启动问题，本文提出了一种基于情感分析和潜在特征映射的跨域推荐算法（CDR-SAFM），该算法通过结合不同域用户评论中隐含的情感信息来实现。与以往的情感研究不同，本文基于三支决策思想将情感分为三类，即积极、消极和中性，通过对用户评论信息进行情感分析来实现。此外，使用潜在狄利克雷分配（LDA）对用户的语义倾向进行建模，以生成潜在的情感评论特征。而且，使用多层感知器（MLP）来获得跨域非线性映射函数，以传递用户的情感评论特征。最后，本文通过在亚马逊数据集的跨域场景中将所提出的CDR-SAFM框架与现有推荐算法进行比较，证明了该框架的有效性。
