Semantic Codebook Learning for Dynamic Recommendation Models

TL;DR

The paper introduces SOLID, a novel framework for dynamic sequential recommendation that uses semantic codebooks to improve personalization, robustness, and efficiency in model parameter generation.

Contribution

It proposes a semantic codebook learning approach that reduces parameter search space and enhances robustness in dynamic recommendation models.

Findings

SOLID outperforms existing methods in accuracy and stability.

Semantic codebooks improve robustness against noisy data.

The framework effectively compresses parameter search space.

Abstract

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.