Reconstructive and Generative Adapters for Robust Cross-Market Sequential Recommendation

The globalization of e-commerce platforms increases the need for effective Cross-Market Sequential Recommendation (CMSR) systems to alleviate data sparsity and the cold-start problem in emerging target markets. Current frameworks, operating under strict privacy regulations, utilize a “pre-train, then fine-tune“paradigm to transfer sequential patterns from data-rich source markets. While selectively transferring self-attention weights mitigates the negative transfer caused by market shifts, existing methods rely heavily on simple Multi-Layer Perceptron (MLP) adapters. These linear transformations often struggle to capture complex, non-linear discrepancies in item popularity and heterogeneous user preferences across different regions. In this paper, we present a comprehensive comparative analysis of advanced Parameter-Efficient Fine-Tuning (PEFT), reconstructive and generative adapters within the CMSR framework. We systematically evaluate Low-Rank Adaptation (LoRA), Encoder-Decoder, Denoising Autoencoder (DAE), and Variational Autoencoder (VAE) architectures against the baseline MLP. Extensive experiments on the real-world XMarket dataset demonstrate that reconstructive and generative paradigms significantly enhance transfer robustness. Notably, the DAE adapter achieves the highest average nDCG@10 performance among the evaluated adapters. The observed improvements are consistent with the hypothesis that reconstructive adapters provide more robust adaptation under target-market sparsity, with the DAE adapter achieving a substantial relative improvement in nDCG@10 over the MLP baseline. Our findings suggest that reconstructive adapters constitute a promising adaptation mechanism for mitigating market discrepancies in decentralized sequential recommendation.