@inproceedings{
lv2024semantic,
title={Semantic Codebook Learning for Dynamic Recommendation Models},
author={Zheqi Lv and Shaoxuan He and Tianyu Zhan and Shengyu Zhang and Wenqiao Zhang and Jingyuan Chen and Zhou Zhao and Fei Wu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=AkLADaZRAZ}
}
Dynamic Sequential Recommendation (DSR) systems, which adapt to users' changing preferences by adjusting their parameters in real time, have emerged as a significant advancement over traditional static models. Despite their advantages, DSR systems face challenges including large item parameter spaces and heterogeneous user-item interactions, which can destabilize the recommendation process. To address these issues, we introduce the Semantic Codebook Learning for Dynamic Recommendation Models (SOLID) framework. SOLID compresses the parameter generation model's search space and utilizes homogeneity within the recommendation system more effectively. This is achieved by transforming item sequences into semantic sequences and employing a dual parameter model, which combines semantic and item-based cues to tailor recommendation parameters. Our innovative approach also includes the creation of a semantic codebook, which stores disentangled item representations to ensure stability and accuracy in parameter generation. Through extensive testing, SOLID has shown to surpass traditional DSR systems, providing more precise, stable, and dynamically adaptable recommendations.~\footnote{Our source code can be referred to \url{https://anonymous.4open.science/r/SOLID-0324}}.
Semantic Codebook Learning for Dynamic Recommendation Models
Zheqi Lv Shaoxuan He Tianyu Zhan Shengyu Zhang Wenqiao Zhang Jingyuan Chen Zhou Zhao Fei Wu