  Dynamic sequential recommendation (DSR) can generate model parameters based
on user behavior to improve the personalization of sequential recommendation
under various user preferences. However, it faces the challenges of large
parameter search space and sparse and noisy user-item interactions, which
reduces the applicability of the generated model parameters. The Semantic
Codebook Learning for Dynamic Recommendation Models (SOLID) framework presents
a significant advancement in DSR by effectively tackling these challenges. By
transforming item sequences into semantic sequences and employing a dual
parameter model, SOLID compresses the parameter generation search space and
leverages homogeneity within the recommendation system. The introduction of the
semantic metacode and semantic codebook, which stores disentangled item
representations, ensures robust and accurate parameter generation. Extensive
experiments demonstrates that SOLID consistently outperforms existing DSR,
delivering more accurate, stable, and robust recommendations.
