Abstract: 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),which explicitly models user preferences to guide the overall tag recommendation.MLP4STR employs a cross-feature alignment MLP framework for sequence feature extraction,aligns the features of text and tags to capture the dynamic interests of users implicit in their historical post and tag information.Finally,it recommends tags by combining post content and user preferences.Experimental results on four real-world datasets show that MLP4STR can effectively learn information from users’ historical behavior sequences in sequential tag recommendation,and the evaluation metric F1@5 shows a significant improvement compared to the optimal baseline algorithms.

Key words: Tag recommendation, Sequential recommendation, Multi-label learning, User preference