Source-linked AI summary
Diffusion Cross-domain Recommendation
Yuner Xuan
TL;DR
Cold-start recommendation is difficult when target users lack interaction history, and existing mapping approaches face challenges in transferring knowledge effectively. DiffCDR uses diffusion to generate target-domain user embeddings, adds alignment and task-oriented learning, and is reported to outperform baselines across cold- and warm-start tasks.
Problem
Cold-start target users lack interaction history, while task-label optimization can overfit noisy labels and sample-level mappings may generalize poorly to unseen samples.
Source: §1 · pp. 1–2Method
DiffCDR uses reverse diffusion conditioned on source-domain user embeddings to generate target embeddings, aligns those embeddings, and incorporates target-label task learning.
Source: §1 · p. 2, §3.1 · p. 3, §3.2 · p. 3Results
Takeaways & Limitations
The study presents diffusion as a mapping approach for transferring auxiliary-domain user preference into target-domain recommendations.
Source: §1 · p. 2A controlled comparison with VAE-based CDRs is difficult because they jointly approximate posterior distributions and optimize recommendation tasks, preventing direct use of pretrained user representations.
Source: §5.2 · p. 8
Abstract
from arXiv · showhide
It is always a challenge for recommender systems to give high-quality outcomes to cold-start users. One potential solution to alleviate the data sparsity problem for cold-start users in the target domain is to add data from the auxiliary domain. Finding a proper way to extract knowledge from an auxiliary domain and transfer it into a target domain is one of the main objectives for cross-domain recommendation (CDR) research. Among the existing methods, mapping approach is a popular one to implement cross-domain recommendation models (CDRs). For models of this type, a mapping module plays the role of transforming data from one domain to another. It primarily determines the performance of mapping approach CDRs. Recently, diffusion probability models (DPMs) have achieved impressive success for image synthesis related tasks. They involve recovering images from noise-added samples, which can be viewed as a data transformation process with outstanding performance. To further enhance the performance of CDRs, we first reveal the potential connection between DPMs and mapping modules of CDRs, and then propose a novel CDR model named Diffusion Cross-domain Recommendation (DiffCDR). More specifically, we first adopt the theory of DPM and design a Diffusion Module (DIM), which generates user's embedding in target domain. To reduce the negative impact of randomness introduced in DIM and improve the stability, we employ an Alignment Module to produce the aligned user embeddings. In addition, we consider the label data of the target domain and form the task-oriented loss function, which enables our DiffCDR to adapt to specific tasks. By conducting extensive experiments on datasets collected from reality, we demonstrate the effectiveness and adaptability of DiffCDR to outperform baseline models on various CDR tasks in both cold-start and warm-start scenarios.
1 INTRODUCTION •
Cold-start users lack interaction history, motivating cross-domain recommendation that transfers knowledge from a source domain. DiffCDR applies diffusion to this transfer and combines it with alignment and target-task learning.
1 INTRODUCTION •
- Cold-start users lack past interactions, and cross-domain recommendation addresses this challenge by transferring knowledge from a source domain with richer records.The target domain may have no historical interactions for these users.Source: §1 · p. 1
- Embedding-based mapping CDRs learn mappings from overlapping users’ embeddings, with prior approaches using direct function learning, meta-learning, or VAE alignment.The mapping function transforms embeddings across domains during prediction.Source: §1 · p. 1
- Task-oriented optimization can overfit noisy labels, while sample-level mappings may restrict generalization to unseen samples by not narrowing domain-distribution divergence.The authors also report that task-oriented learning does not guarantee better performance in their ablations.Source: §1 · p. 2
- Diffusion probability models motivate a mapping module because reverse diffusion transforms noisy data into denoised samples, though inference can require hundreds or thousands of steps.The passage notes that prior work has accelerated diffusion sampling by dozens of times.Source: §1 · p. 2
- DiffCDR uses DIM to generate target-domain user embeddings from source-domain embeddings, ALM to stabilize them, and target-label learning to account for recommendation tasks.Figure 1 depicts learning DIM on overlapping users and generating transferred embeddings for cold-start users.Source: §1 · p. 2, Figure 1 · p. 2
- Experiments on the Amazon reviews dataset cover cold- and warm-start CDR, and the reported ablations identify DIM, ALM, and task-label learning as critical components.The paper describes its evaluation as demonstrating DiffCDR’s effectiveness and adaptability.Source: §1 · p. 2
2 BACKGROUND
The background defines the source–target recommendation setting and describes embedding-based cross-domain mapping. It notes that details of diffusion probabilistic models are deferred to the appendix.
2.1 Problem Setting
- The problem setting has source and target domains, overlapping users, and domain-specific user, item, and rating sets; overlapping users are split for training and evaluation.Training uses overlapping users’ interactions in both domains, while target-domain ratings of test users are used for evaluation.Source: §2.1 · pp. 2–3
2.2 Cross-domain Mapping
- Embedding-based mapping transforms a source-domain user embedding into a target-domain embedding, with a mapping loss minimizing distance from the true target embedding.The recommendation task then combines the mapped user embedding with a target-domain item embedding to score preference.Source: §2.2 · p. 3
2.3 Diffusion Probabilistic Models (DPMs)
- The paper defers the details of diffusion probabilistic models to Appendix A.Source: §2.3 · p. 3
3 MODEL
DiffCDR uses reverse diffusion to generate target-domain user embeddings from source-domain user features, then aligns those embeddings and trains for recommendation tasks. Its pipeline combines diffusion, alignment, and task-oriented losses.
3.1 Diffusion Module (DIM)
- DIM starts from random noise and reverses the diffusion process, conditioned on source-domain user embeddings, to produce transferred target-domain user embeddings.A fast diffusion solver accelerates sampling with fewer steps.Source: §3.1 · p. 3
3.2 Alignment Module (ALM)
- ALM aligns DIM-generated embeddings with ground-truth target-domain representations to reduce randomness; its output is combined elementwise with target-item embeddings for recommendations.The experiments implement ALM with a fully connected layer, while ALM alone does not provide satisfying recommendation results.Source: §3.2 · pp. 3–4, Figure 2 · p. 3
3.3 Loss Computation
- DIM training adds scheduled noise to target-domain user embeddings at a randomly selected timestep and learns to estimate that noise.Source-domain conditioning is randomly masked during training to train conditional and unconditional noise estimates.Source: §3.3 · p. 4
- The ALM loss incorporates the mapping loss, while a task loss uses ratings data directly to adapt the model to specific recommendation tasks.The ALM and task losses share trainable variables and are optimized together with a weighting parameter.Source: §3.3 · p. 4
3.4 Training Strategy
- Training alternates between optimizing the DIM loss and the task-oriented ALM loss in each batch, using a diffusion solver to accelerate DIM inference.The procedure is described in Algorithm 1.Source: §3.4 · p. 4
4 EXPERIMENTS •
Across three Amazon-review CDR tasks, DiffCDR is reported to perform effectively in both cold- and warm-start settings. The experiments also examine its components, transferred embeddings, and throughput.
4.1 Experimental Settings
- In the three Amazon-review tasks, the study reports MAE, RMSE, nDCG, and H@20, averages results over five runs, and evaluates cold-start levels of 20%, 50%, and 80%.Warm-start evaluation fine-tunes on half of test users’ ratings; the split preserves time order to avoid potential leakage.Source: §4.1 · pp. 4–5
- The experiments compare three Amazon-review tasks—Video→Music, Book→Video, and Book→Music—using MAE and RMSE for rating prediction and nDCG and H@20 for ranking.The study uses 5-core subsets and reports mean values from five runs.Source: §4.1 · p. 4
4.2 Cold- and Warm-start Experiments (RQ1)
- DiffCDR shows an advantage in cold-start tasks and better results in all reported warm-start experiments, though Task 2 leaves little room for differences.The warm-start figure distinguishes cold-start and warm-start results by light and dark colors.Source: §4.2 · p. 5, Figure 3 · p. 6
4.3 Ablation Experiments (RQ2)
- The ablations report that task-oriented learning does not always improve performance, DA is at least comparable to mapping-oriented CDRs, and full DAT improves on DA.The authors conclude that DIM, ALM, and target-label learning each contribute to DiffCDR's performance.Source: §4.3 · pp. 5–7, Table 3 · p. 6
4.4 Improvement Analysis (RQ3)
- For Task 1 at β=0.2, the embedding visualization reports that DiffCDR’s transferred embeddings fuse better with target-domain ground truth and have the shortest group distance to TGT.Within DiffCDR, the transferred-to-ground-truth distance shrinks as inference steps increase.Source: §4.4 · p. 7, Figure 4 · p. 7
4.5 Throughput Analysis (RQ4)
- DiffCDR’s throughput is reported as 38.7% of SSCDR for training and 61.1% of PTUPCDR for inference.The authors report these measurements on one 2GHz Xeon(R) CPU core and one P100 GPU core, and note that parallel sampling could further reduce time consumption.Source: §4.5 · p. 7, Table 4 · p. 7
5 RELATED WORK
The related work reviews mapping-based and generative approaches to cross-domain recommendation, then positions DiffCDR as a diffusion-based mapping model. It also distinguishes diffusion’s recommendation use from image generation and discusses practical constraints of generative models.
5.1 Mapping Approach CDRs
- Mapping-based CDRs transfer domain embeddings through learned mappings; DiffCDR instead uses probabilistic diffusion to generate target-domain embeddings conditioned on source-domain guidance.Prior work includes encoder-decoder alignment and personalized mapping functions.Source: §5.1 · pp. 7–8
5.2 VAE Framework CDRs
- VAE-based CDRs align latent spaces using domain-sharing information, but their joint posterior and recommendation optimization makes controlled comparison with DiffCDR difficult.Examples include soft alignment, variational graph encoders, and preference-based user groups.Source: §5.2 · p. 8
5.3 Diffusion Probabilistic Models (DPMs)
- DPMs offer architectural flexibility but can require time-consuming inference; DiffRec applies diffusion to interaction recovery, unlike DiffCDR’s cross-domain transfer task.The related work also surveys guided and large-scale image-generation DPMs and faster sampling approaches.Source: §5.3 · p. 8
6 CONCLUSION AND FUTURE WORK
The conclusion presents DiffCDR as a diffusion-based approach for cross-domain recommendation and reports strong results across cold-start and warm-start tasks. It identifies model architecture and noise schedules as future areas to investigate.
6 CONCLUSION AND FUTURE WORK
- Across three real-world datasets, DiffCDR outperforms baselines on cold-start and warm-start CDR tasks, and ablations find each component contributes to performance.The model combines DIM for transfer, an Alignment Module for stabilization, and a task-oriented loss.Source: §6 · p. 8
- Future work will examine diffusion-model architecture for CDR and the effects of alternative noise schedules in recommendation applications.Source: §6 · p. 8
A BACKGROUND OF DPMS
The background describes DPMs as progressively adding noise to data and learning a reverse process that generates samples from noise. It also outlines how noisy samples and noise-prediction training support this process.
A BACKGROUND OF DPMS
- DPMs gradually add noise through a forward process, then use learned Gaussian transitions to iteratively recover data from a sufficiently noisy state.The reverse transition is approximated as Gaussian when the noise added at each step is small enough.Source: A BACKGROUND OF DPMS · p. 10
- Noised samples at arbitrary steps can be generated from the original data, with the noise schedule specified by ᾱ_t and σ_t.Source: A BACKGROUND OF DPMS · p. 10
- Training approximates the added noise with ε_θ; with constant Σ_θ, the model’s reverse-process mean can be derived from that estimate.Source: A BACKGROUND OF DPMS · p. 10
A.1 Guided DPMs
The section describes classifier-guided and classifier-free ways to condition diffusion generation. Classifier guidance trades sample diversity against accuracy as its gradient scale changes.
A.1 Guided DPMs
- Classifier-guided DPMs modify the noise estimate using classifier gradients for p_ϕ(y|x_t), with s controlling the gradient scale.Source: §A.1 · p. 10
- Larger gradient scale s is claimed to produce more accurate but less diverse samples.Source: §A.1 · p. 10
- Classifier-free guidance replaces the classifier-based term with conditional and unconditional noise estimates, avoiding an additional classifier.Source: §A.1 · p. 10
A.2 Sampling Acceleration
DPM reverse sampling can require hundreds of steps, motivating faster solvers. DPM-Solver exploits the diffusion ODE’s semi-linear structure to produce comparable results with 20 function evaluations.
A.2 Sampling Acceleration
- DPM reverse sampling usually requires hundreds of steps, making it time-consuming and potentially limiting use in high-throughput scenarios.Efficient samplers are described as a central research topic in DPM.Source: §A.2 · p. 10
- Kingma et al. [10] show the SDE has the same transition probability as q(x_t|x_{t-1}); Song et al. [38] describe the reverse process and probability flow ODE.Source: §A.2 · p. 10
- DPM-Solver [19] yields comparable results within 20 function evaluations by treating the diffusion ODE’s linear and nonlinear parts separately, reducing approximation error.Higher-order solvers are claimed to converge with fewer inference steps.Source: §A.2 · p. 10
- DPM-Solver-1 simplifies the nonlinear part across M+1 reverse-process time steps initialized at x_T.Source: §A.2 · p. 10
B ETHICAL CONSIDERATIONS
The paper notes that recommender systems may restrict content diversity, amplify stereotypes or inequalities, and expose users to privacy risks. It recommends policy compliance and giving users control over personalization.
B ETHICAL CONSIDERATIONS
- Personalized recommendations may create filter bubbles, amplify stereotypes or inequalities, and produce biased outcomes that negatively affect some individuals or groups.Source: B ETHICAL CONSIDERATIONS · p. 10
- Collecting user profiles and behavioral records for model training may violate privacy and harm users.Source: B ETHICAL CONSIDERATIONS · p. 10
- Applications should adhere to data policies such as GDPR and give users the right to turn off personal recommendations.Source: B ETHICAL CONSIDERATIONS · p. 10