Semantic Codebook Learning for Dynamic Recommendation Models
This work addresses parameter generation issues in dynamic sequential recommendation systems, which is an incremental improvement for personalized recommendation applications.
The paper tackles the challenges of large parameter search space and sparse/noisy interactions in dynamic sequential recommendation by introducing the SOLID framework, which transforms item sequences into semantic sequences and uses a semantic codebook to compress the parameter generation space. Experiments show SOLID consistently outperforms existing methods with more accurate, stable, and robust recommendations.
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.