# SemaCDR: LLM-Powered Transferable Semantics for Cross-Domain Sequential Recommendation

arXiv: https://arxiv.org/abs/2604.09551 · HTML: https://mlsanity.com/paper/2604.09551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 30 Jan 2026.

## Builds on (most cited first, 11 of 11)

- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [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)
- [Cross-Domain Recommendation: Challenges, Progress, and Prospects](https://mlsanity.com/paper/2103.01696/) (2021)
- [DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2105.03300/) (2021)
- [M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework](https://mlsanity.com/paper/2404.18465/) (2024)
- [LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation](https://mlsanity.com/paper/2409.19925/) (2024)
- [Taxonomy-Guided Zero-Shot Recommendations with LLMs](https://mlsanity.com/paper/2406.14043/) (2024)
- [Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2407.11245/) (2024)
- [SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation](https://mlsanity.com/paper/2506.01705/) (2025)

## Built on by (most cited first, 1 of 1)

- [Memory Has Geometry: Non-Uniform Geometric Memory for Long-Horizon Personalized AI](https://mlsanity.com/paper/2609.17969/) (2026)

## Abstract (arXiv)

Cross-domain recommendation (CDR) addresses the data sparsity and cold-start problems in the target domain by leveraging knowledge from data-rich source domains. However, existing CDR methods often rely on domain-specific features or identifiers that lack transferability across different domains, limiting their ability to capture inter-domain semantic patterns. To overcome this, we propose SemaCDR, a semantics-driven framework for cross-domain sequential recommendation that leverages large language models (LLMs) to construct a unified semantic space. SemaCDR creates multiview item features by integrating LLM-generated domain-agnostic semantics with domain-specific content, aligned by contrastive regularization. SemaCDR systematically creates LLM-generated domain-specific and domain-agnostic semantics, and employs adaptive fusion to generate unified preference representations. Furthermore, it aligns cross-domain behavior sequences with an adaptive fusion mechanism to synthesize interaction sequences from source, target, and mixed domains. Extensive experiments on real-world datasets show that SemaCDR consistently outperforms state-of-the-art baselines, demonstrating its effectiveness in capturing coherent intra-domain patterns while facilitating knowledge transfer across domains.