Sequential Tag Recommendation

• Published in: Ji suan ji ke xue Vol. 52; no. 1; pp. 142 - 150
• Main Author: LIU Bing, XU Pengyu, LU Sijin, WANG Shijing, SUN Hongjian, JING Liping, YU Jian
• Format: Journal Article
• Language: Chinese
• Published: Editorial office of Computer Science 01.01.2025
• Subjects: tag recommendation|sequential recommendation|multi-label learning|user preference
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.240700186

With the development of Internet technology and the expansion of social networks,online platforms have become a significant avenue for people to access information.The introduction of tags has facilitated the categorization and retrieval of information.At the same time,the advent of tag recommendation systems not only makes it easier for users to input tags but also improves the quality of tags.Traditional tag recommendation algorithms typically only consider tags and items,overlooking the crucial role of personal intent when users choose tags.Since tags in a recommendation system are ultimately determined by users,user preferences play a key role in tag recommendation.Therefore,we introduce the user as a subject,and by incorporating the chronological order of users’ historical posts,modeling the task of tag recommendation as a sequential tag recommendation task that is more aligned with real-world scenarios.To address this task,this paper proposes a method named MLP for sequential tag recommendation(MLP4STR)