mlsanityData to 7 Oct 2026

Semantic Codebook Learning for Dynamic Recommendation Models

arXiv 2408.00123 ↗First posted 31 Jul 2024cs.IR cs.AI cs.MMcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 63 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

35 of 63 references.

References · 35

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23 more
  1. 2018Graph HyperNetworks for Neural Architecture Searchcited by 131
  2. 2021Causal Intervention for Leveraging Popularity Bias in Recommendationcited by 116
  3. 2024Data-efficient Fine-tuning for LLM-based Recommendationcited by 103
  4. 2020Controllable Multi-Interest Framework for Recommendationcited by 92
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  23. 2023Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendationcited by 2

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Cited by year

Citing papers per year: 2025: 6.

6 papers.

  1. 2025Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendationcited by 15
  2. 2025Contrastive Cross-Course Knowledge Tracing via Concept Graph Guided Knowledge Transfercited by 7
  3. 2025Cuff-KT: Tackling Learners' Real-time Learning Pattern Adjustment via Tuning-Free Knowledge State Guided Model Updatingcited by 5
  4. 2025Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendationcited by 1
  5. 2025Optimize Incompatible Parameters through Compatibility-aware Knowledge Integrationcited by 0
  6. 2025Tackling Device Data Distribution Real-time Shift via Prototype-based Parameter Editingcited by 0