# LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation

arXiv: https://arxiv.org/abs/2501.19232 · HTML: https://mlsanity.com/paper/2501.19232/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 45 references that are in the map.
- First posted on arXiv 31 Jan 2025.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://mlsanity.com/paper/2404.05961/) (2024)
- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation](https://mlsanity.com/paper/2305.00447/) (2023)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Text Is All You Need: Learning Language Representations for Sequential Recommendation](https://mlsanity.com/paper/2305.13731/) (2023)
- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Leveraging Large Language Models for Sequential Recommendation](https://mlsanity.com/paper/2309.09261/) (2023)
- [GenRec: Large Language Model for Generative Recommendation](https://mlsanity.com/paper/2307.00457/) (2023)
- [ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models](https://mlsanity.com/paper/2305.06566/) (2023)
- [LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation](https://mlsanity.com/paper/2405.20646/) (2024)
- [Zero-Shot Recommender Systems](https://mlsanity.com/paper/2105.08318/) (2021)
- [Contrastive Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2304.03891/) (2023)
- [Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations](https://mlsanity.com/paper/1910.02448/) (2019)
- [Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions](https://mlsanity.com/paper/2311.04590/) (2023)
- [LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation](https://mlsanity.com/paper/2406.12529/) (2024)
- [A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot Transfer](https://mlsanity.com/paper/2401.01497/) (2024)
- [Multi-Modality is All You Need for Transferable Recommender Systems](https://mlsanity.com/paper/2312.09602/) (2023)
- [Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems](https://mlsanity.com/paper/2108.13592/) (2021)
- [Extracting Attentive Social Temporal Excitation for Sequential Recommendation](https://mlsanity.com/paper/2109.13539/) (2021)
- [MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation](https://mlsanity.com/paper/2202.12524/) (2022)
- [Collaborative Cross-modal Fusion with Large Language Model for Recommendation](https://mlsanity.com/paper/2408.08564/) (2024)
- [Pre-trained Neural Recommenders: A Transferable Zero-Shot Framework for Recommendation Systems](https://mlsanity.com/paper/2309.01188/) (2023)

## Built on by (most cited first, 6 of 6)

- [SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence](https://mlsanity.com/paper/2502.08767/) (2025)
- [From LLM-anation to LLM-orchestrator: Coordinating Small Models for Data Labeling](https://mlsanity.com/paper/2506.16393/) (2025)
- [Multi-modal Relational Item Representation Learning for Inferring Substitutable and Complementary Items](https://mlsanity.com/paper/2507.22268/) (2025)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Bridging Textual Profiles and Latent User Embeddings for Personalization](https://mlsanity.com/paper/2605.06981/) (2026)
- [RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations](https://mlsanity.com/paper/2608.19735/) (2026)

## Abstract (arXiv)

Zero-shot cross-domain sequential recommendation (ZCDSR) enables predictions in unseen domains without additional training or fine-tuning, addressing the limitations of traditional models in sparse data environments. Recent advancements in large language models (LLMs) have significantly enhanced ZCDSR by facilitating cross-domain knowledge transfer through rich, pretrained representations. Despite this progress, domain semantic bias -- arising from differences in vocabulary and content focus between domains -- remains a persistent challenge, leading to misaligned item embeddings and reduced generalization across domains. To address this, we propose a novel semantic bias-aware framework that enhances LLM-based ZCDSR by improving cross-domain alignment at both the item and sequential levels. At the item level, we introduce a generalization loss that aligns the embeddings of items across domains (inter-domain compactness), while preserving the unique characteristics of each item within its own domain (intra-domain diversity). This ensures that item embeddings can be transferred effectively between domains without collapsing into overly generic or uniform representations. At the sequential level, we develop a method to transfer user behavioral patterns by clustering source domain user sequences and applying attention-based aggregation during target domain inference. We dynamically adapt user embeddings to unseen domains, enabling effective zero-shot recommendations without requiring target-domain interactions...