  With the development of Internet technology and the expansion of social
networks, online platforms have become an important way for people to obtain
information. The introduction of tags facilitates information categorization
and retrieval. Meanwhile, the development of tag recommendation systems not
only enables users to input tags more efficiently, but also improves the
quality of tags. However, current tag recommendation methods only consider the
content of the current post and do not take into account the influence of user
preferences. Since the main body of tag recommendation is the user, it is very
necessary to obtain the user's tagging habits. Therefore, this paper proposes a
tag recommendation algorithm (MLP4STR) based on the dynamic preference of
user's behavioral sequence, which models the user's historical post information
and historical tag information to obtain the user's dynamic interest changes. A
pure MLP structure across feature dimensions is used in sequence modeling to
model the interaction between tag content and post content to fully extract the
user's interests. Finally tag recommendation is performed.
