Survey of Multi-task Recommendation Algorithms

• Published in: Jisuanji kexue yu tansuo Vol. 18; no. 2; pp. 363 - 377
• Main Author: WEN Minwei, MEI Hongyan, YUAN Fengyuan, ZHANG Xiaoyu, ZHANG Xing
• Format: Journal Article
• Language: Chinese
• Published: Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 01.02.2024
• Subjects: recommendation system; multi-task learning; multi-task recommendation
• ISSN: 1673-9418
• DOI: 10.3778/j.issn.1673-9418.2303014

Single-task recommendation algorithms have problems such as sparse data, cold start and unstable recommendation effect. Multi-task recommendation algorithms can jointly model multiple types of user behaviour data and additional information, to better explore the user’s interests and needs in order to improve the recommendation effect and user satisfaction, which provides a new way of thinking to solve a series of problems existing in single-task recommendation algorithms. Firstly, the development background and trend of multi-task recommendation algorithms are sorted out. Secondly, the implementation steps of the multi-task recommendation algorithm and the construction principle are introduced, and the advantages of multi-task learning with data enhancement, feature identification, feature complementation and regularization effect are elaborated. Then, the application of multi-task learning methods in recommendation algorithms with different sharing models is introduced, and the advantages and disadvantages o