Cross-country recommendation on modern e-commerce platforms is typically deployed with disjoint user and item ID spaces across markets, removing the shared anchors that conventional cross-domain methods rely on. Generative recommendation (GR) mitigates this by mapping items into a shared token space and training a unified model, but existing approaches keep behavior sequences strictly country-specific, so knowledge transfer occurs only at the parameter level and remains absent at the data level. Inspired by code-switching corpora in multilingual natural language processing, we propose CMRec, a cross-country GR framework that injects cross-country supervision at the data level via dual-constrained, context-aware code-mixing. CMRec first learns a shared semantic codebook from multi-modal content and behavioral co-occurrence across countries. It then uses this codebook to synthesize mixed-country sequences via token-level substitutions that satisfy both static (content) and dynamic (e.g., price, audience, popularity) constraints. Finally, it introduces a context-aware loss that reweights mixed samples according to their plausibility in the current sequence. Experiments on two real-world multi-country datasets and an online A/B test show that CMRec substantially improves recommendation quality in data-sparse countries while preserving performance in data-rich countries, achieving +1.77% advertising revenue and +2.64% orders on a large-scale e-commerce platform.
Figures & tables
Method
Recall@10
Recall@100
NDCG@10
NDCG@100
(a) Industrial Dataset (6 countries)
Avg6
A L
B M
C S
Avg6
A L
B M
C S
Avg6
A L
B M
C S
Avg6
A L
B M
C S
SASRec
0.1882
0.1993
0.1867
0.1705
0.2648
0.2807
0.2632
0.2404
0.0941
0.0995
0.0933
0.0853
0.1458
0.1546
0.1448
0.1322
HSTU
0.2117
0.2244
0.2098
0.1929
0.2983
0.3158
0.2957
0.2725
0.1058
0.1122
0.1049
0.0964
0.1642
0.1738
0.1629
0.1498
TIGER
0.2261
0.2396
0.2243
0.2062
0.3187
0.3377
0.3163
0.2909
0.1131
0.1198
0.1122
0.1031
0.1753
0.1858
0.1739
0.1600
REG4Rec
0.2372
0.2517
0.2354
0.2163
0.3342
0.3537
0.3318
0.3057
0.1187
0.1258
0.1177
0.1082
0.1838
0.1949
0.1824
0.1681
Table 1. Performance comparison on the industrial and Amazon M2 datasets. Each metric reports the 6-country/locale average (Avg6) plus three representative ones, with superscripts L / M / S marking large/medium/small volume (Amazon M2 has no M tier). Best results are in bold and second-best are underlined . “ Improv. ” shows the relative improvement (%) over the best baseline.
Variant
Recall@100
NDCG@100
CMRec (Full)
0.3613
0.1972
w/o Token-level distance constraint
0.3447 ( − 4.59%)
0.1894 ( − 3.96%)
w/o Side-info Similarity constraint
0.3573 ( − 1.11%)
0.1947 ( − 1.27%)
w/o Context-aware Adaptive Reweighting
0.3531 ( − 2.27%)
0.1928 ( − 2.23%)
Table 2. Ablation on the Industrial dataset (6-country avg.). Numbers in parentheses are relative gaps vs. Full.
Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains. Recently, generative recommendation (GR) has emerged. It first learns semantic identifiers (SIDs) from item semantics and formulates recommendation as autoregressive generation. However, existing methods face two critical issues: (1) they ignore collaborative correlations across domains during tokenization, and (2) they adopt inefficient decoding strategies, such as beam search, during generation, which hinders real-time deployment. To address these limitations, we propose GenCDSR, an effective and efficient generative framework for CDSR. Specifically, we design a cross-domain hybrid tokenization mechanism with a multi-tower architecture to jointly capture cross-domain commonalities and domain-specific distinctions through hierarchical shared-specific and fine-grained codebooks. Furthermore, we develop a cross-domain serial-parallel decoding strategy that leverages the hierarchical SID structure to partially parallelize generation, significantly reducing inference latency while preserving generation consistency. Experiments on three public datasets show that GenCDSR achieves an average accuracy improvement of 1.5 percent and an average inference latency reduction of 85.1 percent compared with state-of-the-art baselines. The implementation code and datasets are available online: https://github.com/Applied-Machine-Learning-Lab/RecSys2026_GenCDSR.
Yuxuan Hu, Yuhao Wang, Tianbo Huang +4
City University of Hong Kong · Hong Kong, China · ByteDance Inc. +1
Cross-domain recommendation (CDR) transfers preference knowledge across related domains, but federated deployment makes cross-domain alignment difficult because the behavioral anchors that align item spaces, such as overlapping users and shared interaction signals, are often sparse, unavailable, or privacy-sensitive across clients. To address this tension, we revisit federated CDR as generation over a stable semantic item language. By representing items as discrete semantic ID (SID) sequences derived from public item-side metadata, cross-domain item alignment is induced by a shared vocabulary rather than by exchanging private interactions or aligning domain-specific embeddings. Directly federating SID-based generators, however, introduces two design constraints: the SID tokenizer must remain fixed to preserve cross-client token consistency, which creates a semantic-only bottleneck because local collaborative filtering (CF) signals cannot be globally shared or aligned; meanwhile, standard federated averaging can cause negative transfer under domain heterogeneity. To overcome these constraints, we propose FedCGR, a federated generative CDR framework that keeps the item language stable and makes adaptation explicit. FedCGR injects local CF evidence through a reliability-aware semantic interface and trains a prototype-personalized generator that selectively aggregates shared parameters according to domain relatedness while keeping domain-specific quantities local. Experiments on six Amazon cross-domain scenarios show that FedCGR consistently outperforms federated generative baselines and achieves competitive performance against strong sequential and federated CDR methods under both full-ranking and sampled evaluation protocols.
Zhuodong Liu, Hugen Lv, Xiangyu Li +2
Beijing Jiaotong University China · Shanghai Jiao Tong University China · University of Malaya Malaysia +1
Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditional approaches mitigate this via model-specific knowledge distillation from source domains to a target domain. Inspired by the transformative success of synthetic data generation in large language models (LLMs), we introduce Synthetic Cross-domain Augmentation and Learning for Recommendation (SCALR), a framework that generates synthetic user-item interaction events for a target recommendation domain by leveraging observed events from a source domain. SCALR decomposes cross-domain learning into two modular stages. First, it translates observed user events in source domains by framing event generation as estimating the likelihood that a user would interact with a target-domain item, conditioned on their observed interactions in a source domain. Second, downstream models train on these synthetic events as cross-domain learning objectives, where the synthetic events augment the target domain's training data in a model-agnostic manner. Our approach yields statistically significant improvements in online A/B tests on an industrial recommendation platform. To the best of our knowledge, this is among the first works to explicitly frame cross-domain event transfer as synthetic data generation for recommendation systems.