A collaborative filtering recommendation method based on emotional evaluation relations

• Published in: Soft computing (Berlin, Germany) Vol. 28; no. 13-14; pp. 8167 - 8181
• Main Authors: Yin, Yunfei, Ling, Rui, Xu, Youquan, Huang, Faliang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2024; Springer Nature B.V
• Subjects: Application of Soft Computing; Artificial Intelligence; Artificial neural networks; Collaboration; Computational Intelligence; Control; Data collection; Data integration; Deep learning; Design analysis; Engineering; Feedback; Filtration; Mathematical Logic and Foundations; Mechatronics; Music; Neural networks; Recommender systems; Robotics; Systems analysis; Deep learning; Evaluation relationship; Data processing; Emotional relationship; Item recommendation
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-024-09736-6

Improving the accuracy of recommendation systems is a hot research topic, and existing methods have not considered the differences in various types of data. To address this issue, we propose a collaborative filtering recommendation method that combines evaluation relationships and emotional relationships. The emotional relationship is derived from implicit feedback data, and we can exploit the neural network to learn the emotional relationship between users and items. The evaluation relationship is derived from explicit feedback data; similarly, we can exploit the neural network to learn the evaluation relationship between users and items. To obtain emotional and evaluation relationships, we design vector representation and interaction functions based on explicit and implicit feedback data. Moreover, we propose that deep neural networks can be used to integrate evaluation and emotional relationships. To effectively integrate emotional and evaluation relationships, we design multiple data fusion strategies, loss functions, and training strategies. Experimental studies were conducted on five benchmark datasets, and the results show that the collaborative filtering recommendation method based on emotional evaluation relationships can further improve the accuracy of recommendation systems.