# Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation arXiv: https://arxiv.org/abs/2501.11671 ยท HTML: https://mlsanity.com/paper/2501.11671/ ## Key facts - Cited by 4 papers in the map (data through 14 Oct 2026). - Placed in Diffusion recommenders, one of 14 directions in Diffusion models. - Builds on 31 of its 49 references that are in the map. - First posted on arXiv 20 Jan 2025. ## Placed in - Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/) ## Builds on (most cited first, 30 of 31) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015) - [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017) - [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013) - [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021) - [DiffWave: A Versatile Diffusion Model for Audio Synthesis](https://mlsanity.com/paper/2009.09761/) (2020) - [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017) - [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022) - [WaveGrad: Estimating Gradients for Waveform Generation](https://mlsanity.com/paper/2009.00713/) (2020) - [DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models](https://mlsanity.com/paper/2210.08933/) (2022) - [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022) - [Neural Processes](https://mlsanity.com/paper/1807.01622/) (2018) - [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022) - [Diffusion Recommender Model](https://mlsanity.com/paper/2304.04971/) (2023) - [CoNet: Collaborative Cross Networks for Cross-Domain Recommendation](https://mlsanity.com/paper/1804.06769/) (2018) - [CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network](https://mlsanity.com/paper/2005.10549/) (2020) - [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023) - [Plug-in Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2401.02913/) (2024) - [Personalized Transfer of User Preferences for Cross-domain Recommendation](https://mlsanity.com/paper/2110.11154/) (2021) - [DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns](https://mlsanity.com/paper/1905.10760/) (2019) - [Diffusion Models for Generative Outfit Recommendation](https://mlsanity.com/paper/2402.17279/) (2024) - [Contrastive Cross-domain Recommendation in Matching](https://mlsanity.com/paper/2112.00999/) (2021) - [Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users](https://mlsanity.com/paper/2105.04785/) (2021) - [Triple Sequence Learning for Cross-domain Recommendation](https://mlsanity.com/paper/2304.05027/) (2023) - [Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendation](https://mlsanity.com/paper/2202.04920/) (2022) - [Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations](https://mlsanity.com/paper/2104.08490/) (2021) - [Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation](https://mlsanity.com/paper/2007.07085/) (2020) - [Parallel Knowledge Enhancement based Framework for Multi-behavior Recommendation](https://mlsanity.com/paper/2308.04807/) (2023) ## Built on by (most cited first, 4 of 4) - [Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation](https://mlsanity.com/paper/2509.26063/) (2025) - [Align-for-Fusion: Harmonizing Triple Preferences via Dual-oriented Diffusion for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2508.05074/) (2025) - [Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.21021/) (2025) - [Towards A Tri-View Diffusion Framework for Recommendation](https://mlsanity.com/paper/2511.20122/) (2025) ## Abstract (arXiv) Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user representation in the target domain via the transfer of user preference existing in the source domain. Prior efforts mostly follow the embedding-and-mapping paradigm, which first integrate the preference into user representation in the source domain, and then perform a mapping function on this representation to the target domain. However, they focus on mapping features across domains, neglecting to explicitly model the preference integration process, which may lead to learning coarse user representation. Diffusion models (DMs), which contribute to more accurate user/item representations due to their explicit information injection capability, have achieved promising performance in recommendation systems. Nevertheless, these DMs-based methods cannot directly account for valuable user preference in other domains, leading to challenges in adapting to the transfer of preference for cold-start users. Consequently, the feasibility of DMs for CDR remains underexplored. To this end, we explore to utilize the explicit information injection capability of DMs for user preference integration and propose a Preference-Guided Diffusion Model for CDR to cold-start users, termed as DMCDR. Specifically, we leverage a preference encoder to establish the preference guidance signal with the user's interaction history in the source domain. Then, we explicitly inject the preference guidance signal into the user representation step by step to guide the reverse process, and ultimately generate the personalized user representation in the target domain, thus achieving the transfer of user preference across domains. Furthermore, we comprehensively explore the impact of six DMs-based variants on CDR.