Semantic Codebook Learning for Dynamic Recommendation Models

• URL: http://arxiv.org/abs/2408.00123v1
• Date: Wed, 31 Jul 2024 19:25:25 GMT
• Title: Semantic Codebook Learning for Dynamic Recommendation Models
• Authors: Zheqi Lv, Shaoxuan He, Tianyu Zhan, Shengyu Zhang, Wenqiao Zhang, Jingyuan Chen, Zhou Zhao, Fei Wu,
• Abstract summary: Dynamic sequential recommendation (DSR) can generate model parameters based on user behavior to improve personalization of sequential recommendation. 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.
• Score: 55.98259490159084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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