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.

翻译：动态序列推荐（DSR）能够根据用户行为生成模型参数，以提升序列推荐在不同用户偏好下的个性化程度。然而，该方法面临参数搜索空间庞大以及用户-物品交互稀疏且含噪声的挑战，这降低了所生成模型参数的适用性。用于动态推荐模型的语义码本学习（SOLID）框架通过有效应对这些挑战，在DSR领域取得了显著进展。SOLID通过将物品序列转换为语义序列，并采用双参数模型，压缩了参数生成的搜索空间，同时利用了推荐系统内部的同质性。语义元码和语义码本的引入——后者存储解耦的物品表示——确保了参数生成的鲁棒性与准确性。大量实验表明，SOLID在各项指标上持续优于现有DSR方法，能够提供更准确、稳定且鲁棒的推荐结果。