Knowledge-aware diffusion contrastive learning improves multi-level recommendation

Recommender systems have become the invisible infrastructure of modern digital life, deciding which films appear in a streaming queue, which products fill an online shopping cart, and which news stories surface at the top of a social feed. Yet the algorithms behind these systems face persistent obstacles that limit how well they can actually understand a user’s tastes. Two of the most stubborn problems are data sparsity, where most users interact with only a tiny fraction of the available items, and noise, where irrelevant or misleading connections in the knowledge structures used to enrich recommendations degrade performance instead of improving it. A newly published study in the Journal of Intelligent Information Systems proposes a framework that attacks both problems at once, combining a locally deployed large language model with a diffusion-based denoising process and a multi-level contrastive learning strategy.

The framework, called KMDCL, was developed by Zhijia Yu, Daofu Gong, and Lei Tan of the College of Cyberspace Security at Information Engineering University in Zhengzhou, China. Their central insight is that knowledge graphs, the vast structured networks of entities and relationships that describe facts about items, hold enormous potential for recommender systems, but that potential is frequently wasted. Knowledge graphs encode rich factual information and semantic associations between items, for example linking a film to its director, genre, era, and subject matter, and this information can help a model understand what an item is even when few users have interacted with it. However, as the authors note, not all information in a knowledge graph carries the same relevance or value for a given recommendation task. Connections that are factually true may still be semantically useless or actively misleading when the goal is to predict user preference.

Knowledge graphs also suffer from significant inherent sparsity, meaning many real-world entities have only a handful of documented relationships, and in some cases the connections retrieved from a knowledge graph introduce extra noise into the training signal. To make matters more complicated, most existing approaches model user behavior collaborative signals, the patterns of interaction among users and items, and knowledge graph semantic signals separately, then attempt to fuse them. KMDCL takes a different path by first purifying and enhancing the knowledge graph itself before merging it with interaction data.

The first stage of the pipeline addresses the sparsity problem through knowledge graph completion. Rather than relying on a remote, commercial language model, the researchers deploy a large language model locally and use it as the engine of a completion strategy. Local deployment matters for several reasons. It keeps sensitive interaction data and query context inside the operator’s infrastructure, it reduces latency and dependence on external services, and it gives the system full control over how the model’s factual knowledge is prompted and constrained. The locally hosted LLM is used to infer missing entities and plausible relations, filling in gaps in the graph so that items that were previously isolated or thinly connected gain additional semantic context. This enriched connectivity gives downstream graph-based encoders more material to work with, particularly for long-tail items whose collaborative signals are weak.

Completion alone, however, would be counterproductive if the newly added relations were noisy, and this is where the second innovation comes in. The team proposes a knowledge graph denoising method based on diffusion models, the same class of generative techniques that has transformed image synthesis but which operates here in the structured world of graphs. Diffusion models work by progressively corrupting data with noise in a forward process and then learning to reverse that corruption in a learned denoising process. Building on principles popularized by denoising diffusion probabilistic models and extended to discrete graph structures by methods such as DiGress, the KMDCL denoiser learns the distribution of trustworthy relations in the knowledge graph. Low-confidence, noisy relations are filtered out as the diffusion process separates genuine semantic structure from statistical artifacts. The combination is conceptually elegant: the LLM completes the graph by adding likely true and useful connections, and the diffusion model prunes the graph by removing connections that are unlikely to be true or useful. The result is a knowledge graph that is simultaneously denser and cleaner than the original.

With the optimized knowledge graph in hand, the framework moves to its third major component. KMDCL combines the denoised knowledge graph with the user-item interaction graph, the bipartite network of clicks, purchases, ratings, or other behavioral signals, to construct a multi-view representation space for items. In this space, each item is described from more than one perspective: the semantic view, derived from the knowledge graph, and the behavioral view, derived from how users actually interact with it. Multi-view representations are a well-established strategy for building robustness, since an item that looks similar to another in behavior but not in semantics, or vice versa, can be distinguished rather than conflated.

To sharpen the discrimination of these representations further, the authors introduce a contrastive learning mechanism operating at multiple levels. Contrastive learning, which has become a dominant paradigm in recommendation research since methods such as self-supervised graph learning and knowledge graph contrastive learning for recommendation, trains a model by pulling together the representations of related or augmented views of the same entity while pushing apart the representations of unrelated entities. Applied here across the multi-level structure of the combined graphs, the mechanism forces the item embeddings to capture information that is both semantically meaningful, thanks to the curated knowledge graph, and behaviorally relevant, thanks to the interaction data. Theoretical work and prior empirical studies, including multi-level interactive contrastive learning approaches presented at CIKM, have suggested that such hierarchical contrastive objectives are particularly effective when signals live at different granularities, and KMDCL integrates that principle directly into its architecture.

The researchers evaluated their framework through extensive experiments on three public datasets, benchmarking it against a variety of baseline models that represent the major lineages of modern recommendation research. These baselines span the field’s history, from classical matrix factorization techniques described by Koren and colleagues, through Bayesian personalized ranking from implicit feedback, to neural collaborative filtering, and onward to graph-based methods such as Knowledge Graph Convolutional Networks, the Knowledge Graph Attention Network KGAT, LightGCN, and more recent diffusion-inspired and contrastive approaches including DiffKG and knowledge graph cross-view contrastive learning. Evaluation followed established practice in the field, using the sampled metrics methodology of Krichene and Rendle to assess top-n recommendation quality.

Across all three datasets, KMDCL achieved significant performance improvements over the range of baselines. The ablation-driven logic of the design was validated in practice: completing the knowledge graph with the locally deployed LLM, denoising it with the diffusion model, and training with multi-level contrastive objectives each contributed to the final result, and the combination proved more effective than any single intervention. The authors report that the method effectively alleviates the challenges posed by both data sparsity and knowledge graph noise, and that it enhances the learned representations in ways that translate directly into better recommendation performance.

The significance of the work extends beyond a single leaderboard result. It addresses a recurring tension in knowledge-aware recommendation: knowledge graphs promise a cure for sparsity, but their own imperfections can reintroduce the very noise that harms recommendation quality. By treating graph completion and graph denoising as complementary generative tasks, one powered by the factual and reasoning capabilities of a locally hosted large language model and the other by the statistical rigor of diffusion processes, the framework offers a template that other researchers can adapt. It also reflects a broader trend in 2026 research toward integrating large language models into recommendation pipelines not as end-to-end recommenders, which remain computationally expensive and prone to hallucination, but as targeted semantic tools for specific structured tasks such as entity linking, relation inference, and graph completion.

There are also practical implications for deployment. The choice to run the LLM locally, rather than calling a cloud service, suggests a design that could be adopted by organizations with privacy constraints or limited external API budgets, since the model is invoked during knowledge graph construction rather than at every recommendation request. Meanwhile, the diffusion-based denoising stage runs at graph-construction time as well, meaning the purified graph can be cached and reused, keeping the online inference cost comparable to that of conventional graph neural network recommenders.

The work sits within a rapidly growing body of literature on diffusion models in recommendation, which has expanded from general diffusion recommender models and hyperbolic diffusion approaches to diffusion-based dual-graph architectures for multimodal recommendation and causal diffusion methods aimed at out-of-distribution robustness. KMDCL’s contribution to that wave is to show that diffusion is as useful for cleaning the knowledge that feeds a recommender as it is for generating or refining the recommendation signals themselves.

For readers, the takeaway is straightforward: the next generation of recommendation engines may not simply observe what people click. They will consult structured maps of world knowledge, repair and purify those maps with generative AI, and then learn from both behavior and knowledge simultaneously, discarding noise along the way. As Yu, Gong, and Tan demonstrate, when these ingredients are carefully orchestrated, recommendations become more accurate even in the sparse, noisy environments where recommender systems have historically struggled most.