The new architecture, called RPGCN for Relation-Preserving Graph Convolutional Network, was developed by an international team of researchers led by Sang-Woong Lee of Gachon University in South Korea, working with collaborators at institutions spanning Oman, Taiwan, Vietnam, India, Jordan, Thailand, Azerbaijan and Iran. Their central claim is that conventional graph convolutional approaches to recommendation suffer from two related failures: oversmoothing, in which the representations of different nodes gradually become indistinguishable as information is aggregated layer by layer, and relational dilution, in which the fine-grained character of individual user-item interactions is washed out when signals from many neighbors are averaged together. Both problems are especially acute in sparse and heterogeneous datasets, where the graph is riddled with missing links and nodes of very different degrees.

To understand why this matters, it helps to picture how a graph convolutional network actually works in a recommendation setting. The user-item interaction data is typically represented as a bipartite graph, with users on one side and items on the other, and edges connecting users to the products they have rated, purchased or watched. A graph convolutional layer works by letting each node collect feature information from its neighbors and blend it into its own representation. After a few rounds of this message passing, a user node has absorbed signals from the items it interacted with, and those items have in turn absorbed signals from other users, so the network builds up an embedding that encodes a user’s tastes in terms of the broader interaction structure. The approach, popularized by methods such as Neural Graph Collaborative Filtering and LightGCN, has become a cornerstone of modern collaborative filtering.

The trouble, the RPGCN authors contend, is that this aggregation is lossy in ways that matter. When a user’s representation is blended with those of all their neighbors, the distinctive signature of each individual relationship is diluted. Stacking more layers to capture longer-range structure makes things worse, because repeated averaging drives all node embeddings toward a common value, the oversmoothing phenomenon that has plagued deep graph networks since their inception. In a sparse dataset, where most users have interacted with only a handful of items, the dilution is compounded: there is simply not enough signal to begin with, and the standard aggregation throws away what little there is.

RPGCN attacks the problem with a hybrid design that unifies two fusion strategies, early fusion and intermediate fusion, with a multi-branch graph attention backbone. Rather than forcing all relational information through a single convolutional pathway, the model maintains specialized User-GCN and Item-GCN branches that explicitly preserve both local and global user-item interactions. Graph attention networks, which learn to weight the contribution of each neighbor differently instead of averaging uniformly, allow the model to decide which relationships deserve to be emphasized and which can be safely down-weighted. By keeping user-side and item-side processing separate before combining them, the architecture prevents the relational structure of each side of the bipartite graph from being smeared into the other.

The second pillar of the design is an auxiliary self-supervised learning task. Self-supervision has emerged in recent years as a powerful tool for recommendation, notably in methods such as Self-supervised Graph Learning, because it lets a model extract training signal from the data itself rather than depending entirely on sparse explicit ratings. In RPGCN, the auxiliary task strengthens the robustness of the learned representations in sparse scenarios, effectively giving the network a second objective that encourages it to retain structural information even when the interaction matrix is thin. This matters for the cold-start regime, where new users or items have few or no recorded interactions and conventional models struggle to place them meaningfully in the embedding space.

The empirical case for the approach rests on experiments across five benchmark datasets that together span a wide range of recommendation conditions: MovieLens 100K and MovieLens 1M, the classic film-rating collections; ModCloth and RentTheRunway, clothing datasets known for their sparsity and the inclusion of user attributes such as fit feedback; and Epinions, a consumer review network with trust relations layered on top of ratings. The evaluation covered both predictive metrics, which measure how accurately the model reconstructs ratings, and ranking metrics, which measure how well it orders items for each user. Specifically, the team reported results on AUC, RMSE, MAE, NDCG and Hit Rate at ten.

According to the study, RPGCN consistently surpassed state-of-the-art baselines across all of these metrics on all five datasets. The consistency is the notable part. A model that wins on rating accuracy but loses on ranking, or vice versa, may simply be optimizing a different notion of quality; a model that improves both simultaneously suggests it has genuinely captured more of the underlying relational structure. The authors attribute the gains to RPGCN’s ability to retain fine-grained relational information that competing architectures lose to oversmoothing and dilution, particularly in the sparsest and most heterogeneous of the tested environments, where the difference between preserving and discarding relational detail is most consequential.

The work sits within a rapidly expanding research program that applies graph learning to recommendation. The cited literature traces a clear arc from matrix factorization techniques, which dominated the field for a decade after their 2009 popularization, through graph convolutional networks following Kipf and Welling’s foundational 2016 work, to recent hybrids that combine graph attention, reinforcement learning and fusion strategies. The same research group has previously published a progressive graph attention-based deep reinforcement learning recommender and a synergetic fusion-based graph convolutional approach for link prediction in social networks, and RPGCN extends that line by making relation preservation an explicit architectural commitment rather than an incidental byproduct of message passing.

For the industry, the implications are practical. Recommendation quality translates directly into engagement, revenue and user satisfaction, and the datasets on which RPGCN excels, sparse and heterogeneous ones, are precisely the conditions most real platforms face. A model that holds up on clothing retail data with minimal ratings, or on review networks with tangled trust structures, is more likely to survive contact with production traffic than one tuned only on dense movie ratings. The authors note that their code and data will be made available on request, and the evaluation was carried out within the widely used RecBole framework, which should make replication and comparison straightforward. Whether relation-preserving designs become a standard component of the recommendation stack remains to be seen, but the study offers a concrete demonstration that in graph-based recommendation, what you keep can matter as much as what you learn.

Subject of Research: A relation-preserving graph convolutional network architecture for improving node feature prediction and recommendation quality in sparse user-item graphs

Article Title: RPGCN: A relation-preserving graph convolutional network for enhanced node feature prediction in recommender systems

Article References: Lee, S.-W., Ali, S., Rahmani, A. M., Zare, G., Alamdari, P. M., Khoshvaght, P., Hourani, M., Porntaveetus, T., & Hosseinzadeh, M. (2026). RPGCN: A relation-preserving graph convolutional network for enhanced node feature prediction in recommender systems. Neural Computing and Applications, 38(19), Article 766. https://doi.org/10.1007/s00521-026-12480-7

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12480-7

Keywords: recommender systems, graph convolutional network, graph attention networks, collaborative filtering, self-supervised learning, oversmoothing, bipartite graphs, node feature prediction, hybrid fusion, MovieLens, cold start problem, deep learning

The core insight behind the work is that no single modelling strategy captures everything that matters about a user’s preferences. Collaborative filtering, the classic approach that infers taste from patterns of who liked what, struggles when interaction data is sparse — and interaction data is almost always sparse, because any individual user has rated or clicked on only a tiny fraction of the items in a large catalogue. Knowledge graphs, which encode structured facts about items such as actors, genres, directors, or product categories, offer external semantic structure, but exploiting them effectively requires multi-hop reasoning across relations. Sequential models can capture the order in which a user consumed items, yet they say nothing about the underlying attributes of those items. Rather than choosing one lens, NEXUS-Rec fuses several, and the authors report that the fusion is what drives its performance gains.

At the representation level, the framework begins with Contrastive Graph Learning, a self-supervised technique that trains user–item encoders by augmenting the interaction graph and learning representations that remain consistent across the augmented views. Crucially, the augmentation is relation-aware: the authors apply heterogeneous perturbation rates across different knowledge-graph relation types, so that the distortions used to train the encoder respect the fact that some semantic relations are more informative or more reliable than others. This is paired with a Knowledge Graph Attention Network, or KGAT, which performs multi-hop, relation-aware propagation over the structured knowledge. A bilinear attention mechanism is layered on top to capture higher-order feature interactions, allowing the model to weigh combinations of attributes rather than treating each attribute in isolation.

Two further components address noise and time. To temper spurious correlations and compress noisy features, the authors incorporate an enhanced Variational Information Bottleneck, an information-theoretic tool that forces the model to squeeze its representations through a constrained channel, keeping only the information that is genuinely predictive and discarding the rest. Sequential dynamics, meanwhile, are handled by what the authors call Temporal Attention Flow, which models time-ordered consumption patterns with an exponential recency decay mechanism. That decay constrains the temporal receptive field — the window of past behaviour the model attends to — so that what a user did yesterday counts for more than what they did months ago, without the model drowning in irrelevant history.

The final ingredient, and arguably the most distinctive one, is a rank-aware Gated Fusion mechanism that combines the outputs of all the components. Global learnable gates determine how much weight each component receives in general, while candidate-level rank-agreement features let the fused score adapt to each individual user and each candidate item. The result is a recommendation score that is not a static blend but a dynamic one, shifting its reliance between collaborative, knowledge-based, temporal, and other signals depending on the situation. Because the gates are learnable and inspectable, they double as an explanation: the system can report, in numbers, how much each module contributed to a given ranking.

The empirical results are striking. On the widely used MovieLens-1M benchmark, NEXUS-Rec achieves an NDCG@10 — a standard ranking-quality metric that rewards placing relevant items near the top of the list — of 0.4006. That significantly outperforms every baseline tested, including the strongest classical method, BPR-MF, which scores 0.3368, an improvement of 18.96 percent with a statistical significance of p < 10⁻²⁴. It also beats KGAT itself, at 0.3341 with p < 10⁻²⁸, and surpasses recent self-supervised graph methods including SGL, SimGCL, and LightGCL, all with p-values below 10⁻¹⁰⁰, a level of statistical separation that is rare in recommendation research. To probe generality, the authors added a second benchmark, MovieLens-100K, where NEXUS-Rec again attained the best NDCG@10, at 0.3712, improving over the strongest external baseline, BPR-MF, by 20.9 percent.

Just as important as the headline numbers is the ablation analysis, in which the authors systematically removed each component and measured the damage. Every component proved to contribute substantively: removing any one of them caused a performance drop of between 22 and 35 percent, with ItemKNN, the content-based module, and KGAT producing the largest individual effects. This matters because multi-component architectures in machine learning sometimes contain parts that add complexity without adding value; here, the evidence suggests that each of the five signals is pulling real weight, and that the fusion is not merely averaging away redundancy but genuinely integrating complementary information.

The interpretability results offer a glimpse of what transparent recommendation might look like in practice. The learned importance gates revealed that collaborative filtering received the highest gate activation, at 0.317, followed by temporal attention at 0.288 and popularity signals at 0.159. In other words, for the datasets studied, the model leaned most heavily on patterns of collective user behaviour, then on the recency-weighted sequence of each user’s own consumption, and least on raw popularity. Because these gates are exposed per instance, the system can produce explanations of the form that a given recommendation was driven primarily by similar users’ preferences, with a substantial secondary contribution from the user’s recent viewing trajectory — a far more informative account than the generic ‘because you watched X’ justifications that dominate commercial platforms today.

The broader significance of the work lies in its attempt to reconcile two goals that are often treated as a trade-off: accuracy and explainability. Post-hoc explanation methods, which try to rationalise a black-box model’s outputs after the fact, have been criticised for producing plausible-sounding but unreliable narratives. By building interpretability into the architecture itself — through gates whose activations are part of the model’s actual computation rather than a bolt-on analysis — NEXUS-Rec offers explanations that are causally tied to how the score was produced. The rank-aware design also means the explanation can vary from one candidate to the next, reflecting the reality that different items may be recommended for different reasons.

Caveats remain, as they do for any benchmark study. The evaluation rests on two movie datasets, and performance on other domains — e-commerce, music, news — would need to be demonstrated separately. The framework is also more computationally elaborate than single-signal baselines, which raises questions about deployment at industrial scale. Still, the study, which the authors report received no specific funding and uses only publicly available benchmark datasets, represents a notable step toward recommender systems that are simultaneously more accurate and more accountable. As regulators and users increasingly demand to know why algorithms make the choices they do, architectures like NEXUS-Rec suggest that the answer need not come at the cost of performance — and that the future of recommendation may belong to systems that can show their work.

Subject of Research: A neural, explainable recommender system that fuses knowledge graph learning, contrastive encoders, variational information bottleneck, and temporal attention for improved ranking accuracy.

Article Title: Nexus-rec: a neural explainable unified system for knowledge graph-enhanced recommendations

Article References: Pasupuleti, M., & Satapathy, S. M. (2026). Nexus-rec: a neural explainable unified system for knowledge graph-enhanced recommendations. Complex &amp; Intelligent Systems. https://doi.org/10.1007/s40747-026-02496-w

Image Credits: AI Generated

DOI: 10.1007/s40747-026-02496-w

Keywords: recommender systems, knowledge graph, contrastive learning, graph attention networks, variational information bottleneck, sequential recommendation, explainable AI, collaborative filtering, MovieLens, NDCG, gated fusion, self-supervised learning

The system, called IGDHRS for intelligent graph-based deep hybrid recommender system, was developed by Milad Payandeh, Seyed Mahdi Jameii, and Mostafa Haghi Kashani of the Department of Computer Engineering at Islamic Azad University in Iran. Their starting point is a familiar taxonomy: recommender systems generally fall into collaborative filtering, which learns from patterns in user behavior; content-based filtering, which matches item attributes to user preferences; and hybrid models that blend the two. Each family carries its own liabilities. Collaborative approaches collapse when interaction data is thin or missing, while content-based methods struggle to capture the subtle, evolving tastes that ratings reveal. The Iranian team’s answer is a hybrid that treats the user population itself as a graph and then learns rich representations from that structure.

The first architectural step is the construction of a user–user similarity graph, in which nodes represent individual users and edges encode how alike their rating behaviors are. Building such a graph requires deciding, for every pair of users, whether their measured similarity is strong enough to justify a connection, and that decision hinges on a similarity threshold. Set the threshold too low and the graph becomes a dense tangle of weakly related users, diluting the signal; set it too high and the graph fragments, cutting off genuinely helpful neighborhood information. Rather than fixing this threshold by hand, the researchers let it adapt dynamically using learning automata, a class of reinforcement-driven stochastic decision units that adjust their actions based on feedback from the environment. In effect, the system tunes its own notion of who counts as a similar user as training proceeds.

Learning automata deserve a closer look because they are a departure from the gradient-based optimization that dominates modern deep learning. An automaton maintains a probability distribution over a set of possible actions, selects one, observes a reward or penalty, and updates its probabilities accordingly. Over many iterations it converges toward actions that consistently earn rewards. In IGDHRS, that feedback loop nudges the similarity thresholds toward values that ultimately improve recommendation quality, a form of automatic hyperparameter adaptation that relieves engineers of a delicate tuning burden and lets the graph topology evolve to match the data at hand.

To give the graph more expressive power, the authors enrich each user node with auxiliary demographic information, including age, gender, and occupation. This is a deliberate countermeasure to the cold-start problem: even a brand-new user with zero ratings carries demographic attributes that can anchor them in the similarity graph, linking them to established users with comparable profiles. Previous work has shown that demographic profile expansion can buffer sparse rating matrices, but integrating such side information directly into a graph neural architecture is what makes this system distinctive. The demographics act as a bridge across the rating desert, allowing information to propagate from well-modeled users to newcomers along graph edges that would not otherwise exist.

At the heart of the architecture sits the study’s central technical contribution: a deep denoising graph convolutional autoencoder, abbreviated DDGCAE. An autoencoder is a neural network trained to compress its input into a low-dimensional latent code and then reconstruct the original signal from that code, forcing it to learn the essential structure of the data. A denoising autoencoder raises the stakes by deliberately corrupting the input, for example by masking or perturbing entries, and requiring the network to recover the clean version, which cultivates robustness to the missing and noisy values that pervade real rating matrices. A graph convolutional autoencoder extends this idea to graph-structured data: graph convolution layers aggregate information from each node’s neighbors, so the learned embeddings encode not just a user’s own behavior but the behavior of the surrounding network neighborhood.

Combining all three ingredients means that DDGCAE operates on an enriched, adaptively thresholded user similarity graph, learns compressed representations by reconstructing denoised graph signals, and produces latent user profiles that capture both interaction patterns and demographic context. Those latent representations then drive rating prediction. The design echoes and extends a lineage of prior systems, from classic autoencoder-based collaborative filtering models such as AutoRec and collaborative denoising autoencoders to graph-based methods like Neural Graph Collaborative Filtering and LightGCN, but the authors argue that the joint interplay of denoising, graph convolution, and automata-driven graph construction is what sets their approach apart.

The team implemented IGDHRS in Python and evaluated it on four widely used benchmark datasets spanning different scales and domains: MovieLens 100K, MovieLens 1M, a stratified random sample of the Netflix Prize data, and the Amazon Movies and TV dataset. Because the Netflix and Amazon corpora are enormous, the researchers extracted stratified random samples, and they have made the sampling scripts and generated sample indices publicly available in a GitHub repository, alongside the full system implementation, a level of openness that supports reproducibility. Performance was measured with standard regression and ranking metrics: root mean squared error and mean absolute error to quantify how far predictions deviate from true ratings, and precision and recall to gauge the quality of the top recommendations actually surfaced to users.

The reported results are striking. Across all four datasets, the proposed system significantly outperformed several state-of-the-art comparison methods, and the advantages held in the conditions that matter most: domains with higher data sparsity and datasets where demographic information was partially missing. The authors attribute this robustness directly to the three-way combination of auxiliary user data, learning automata, and the DDGCAE architecture, and they specifically highlight improved resilience to cold-start situations, the scenario in which traditional collaborative filtering degrades most severely. Statistical rigor was addressed as well, with the study employing cross-validation practices and nonparametric significance testing in the tradition of the Wilcoxon ranking method to substantiate that observed gains were not artifacts of a lucky split.

For the broader field, the study suggests that the path past the cold-start and sparsity bottleneck may lie not in any single clever component but in architectures that let multiple adaptive mechanisms reinforce one another. Graph structures supply the relational scaffolding, denoising objectives harden the learned embeddings against missing data, demographic side channels keep new users connected from day one, and learning automata quietly optimize the structural choices that humans would otherwise guess. The code and data are public, the benchmarks are the community’s standards, and the message is clear: recommender systems that can rebuild their own wiring while learning from corrupted signals are a promising blueprint for the next generation of personalization engines, from streaming catalogs to e-commerce and beyond.

Subject of Research: A deep graph-based hybrid recommender system addressing cold-start and data sparsity using learning automata

Article Title: An intelligent recommender system based on deep denoising graph convolutional autoencoder and learning automata

Article References: Payandeh, M., Jameii, S. M., & Kashani, M. H. (2026). An intelligent recommender system based on deep denoising graph convolutional autoencoder and learning automata. International Journal of Data Science and Analytics, 22(1), Article 312. https://doi.org/10.1007/s41060-026-01293-5

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01293-5

Keywords: recommender systems, graph convolutional networks, autoencoders, learning automata, cold-start problem, data sparsity, collaborative filtering, deep learning, MovieLens, Netflix Prize, Amazon dataset, hybrid recommendation

The technique belongs to a family of attacks known as transfer-based adversarial attacks. In such attacks, an adversary cannot peer inside the target recommendation system, which operates as a black box guarded by commercial secrecy. Instead, the attacker builds a surrogate model that mimics the target’s behavior, trains the surrogate on publicly available data, and then injects carefully constructed fake user profiles designed to manipulate the surrogate’s recommendations. The hope is that these poisoned profiles will transfer: that when injected into the real, unknown target system, they will produce the same distortion, promoting a chosen item or burying a competitor. This scenario, often called a shilling attack, has been studied since the early days of collaborative filtering, but deep learning has dramatically raised the stakes, because modern neural recommenders are both more powerful and, in some respects, more susceptible to carefully aimed perturbations.

The core insight behind RecGP comes from an unexpected corner of deep learning theory: the geometry of the loss landscape. When an adversarial example is optimized on a surrogate model, it typically settles into a region where the loss surface is sharp—a narrow spike surrounded by steep gradients. Such solutions perform superbly on the surrogate but generalize poorly, because they are exquisitely sensitive to small changes in model parameters. Two recommendation systems, even trained on the same data, will have slightly different internal weights, and an adversarial example perched on a sharp peak will lose its effectiveness under that variation. Flat regions of the loss landscape, by contrast, exhibit small gradients and gentle slopes, meaning the adversarial example’s effect is stable even when parameters shift between the surrogate and the unknown target. The same flatness principle underlies sharpness-aware minimization, a technique developed to improve model generalization in entirely benign contexts, and the authors adapt it here for offensive purposes.

RecGP operationalizes this insight by adding a gradient penalty to the optimization process that generates fake users. As the attacker searches for adversarial perturbations, the penalty regularizes the magnitude of the gradients, steering the search away from sharp peaks and toward flat basins of the loss surface. In effect, the method asks not merely, ‘Which fake user fools this surrogate most effectively?’ but rather, ‘Which fake user fools this surrogate in a way that is robust to the inevitable differences between models?’ The authors frame this as a recommendation-specific reformulation, because recommendation data differs fundamentally from the continuous image data where flatness-aware attacks were first explored. A fake user profile in a recommender is not a subtly shifted photograph; it is a discrete collection of interactions, and the attack must respect the semantic structure of that discrete space.

That discrete structure creates a computational challenge, which the team addressed with a second contribution: RecGP-RS, or Recommender Systems Gradient Penalty with Resource-efficient Sampling. Computing true second-order gradient information—needed to assess the flatness of the landscape—is expensive, particularly over the large interaction spaces typical of real-world recommender systems. RecGP-RS sidesteps the cost through semantic-aware neighborhood sampling, which selects representative neighbors of each perturbation in the discrete interaction space while preserving semantic consistency, ensuring that sampled neighbors correspond to plausible user behaviors rather than arbitrary noise. Around these sampled neighbors, the method approximates second-order gradients using first-order interpolation, capturing the essential curvature information at a fraction of the computational price. The result, according to the paper, is a variant that retains 98 percent of the attack efficacy of the full method while reducing GPU memory consumption by 42 percent—a substantial saving that matters when attacks must be staged against large-scale production-like systems.

The empirical evaluation spanned two widely used benchmark datasets: MovieLens-1M, a canonical collection of roughly one million movie ratings maintained by the GroupLens research group, and Gowalla, a location-based social network dataset distributed through the Stanford Network Analysis Project. Across eight target recommendation models, RecGP achieved an average attack success rate improvement of approximately eight percent over existing baseline attack methods on the Gowalla dataset. The target models examined in the broader literature on which this work builds include the standard architectures of the field: neural collaborative filtering, Bayesian personalized ranking, collaborative denoising autoencoders, and matrix factorization approaches, among others. The breadth of improvement across diverse architectures is the transferability claim’s real substance—an attack that only worked against one model family would be of limited concern, but a method that reliably degrades many different recommenders suggests a structural weakness in how these systems learn from behavioral data.

The practical implications are sobering. Recommendation systems are not merely convenience features; they are revenue engines. A seller who can promote products through injected fake users can distort marketplace competition, and a malicious actor who can suppress content can shape public opinion. Earlier generations of shilling attacks required large volumes of hand-crafted fake profiles and were relatively easy to detect because they followed stereotyped patterns. Learning-based attacks such as the one developed here generate profiles optimized by gradient descent, which can be subtler and harder to flag. The flatness technique makes them more portable across the heterogeneous collection of models that platforms actually deploy, meaning a profile set crafted once could plausibly threaten several services rather than one. The study also notes that RecGP builds on earlier transferability work by the same group, including methods based on Nesterov momentum and multi-model integration and fine-tuning, indicating a sustained research trajectory into how adversarial examples move between recommender architectures.

From a defensive standpoint, the research is valuable precisely because it illuminates the mechanism of failure. If sharp loss regions are what make adversarial examples brittle and flat regions what make them dangerous, then defenders have a concrete signal to target. Detection systems could look for interactions that sit suspiciously in flat regions of the platform’s own loss surface, or training procedures could incorporate flatness-aware objectives that make recommendation models inherently less sensitive to small numbers of poisoned profiles. The work also joins a broader conversation about loss landscape geometry in machine learning security, echoing findings from computer vision where flat local maxima have been linked to improved adversarial transferability, and from theoretical studies of the embedding principle of loss landscapes in deep neural networks. The transferability problem, once considered a vision-specific curiosity, now demonstrably spans the recommender domain.

The research team, based at Anhui University’s Key Laboratory of Intelligent Computing and Signal Processing and its Artificial Intelligence Institute, with a collaborator at Tsinghua University, was supported by the National Natural Science Foundation of China and provincial research programs, and used Anhui University’s high-performance computing platform. Caihong Wu and Hai Chen contributed equally to the work, with Fulan Qian serving as corresponding author. As recommendation systems grow more embedded in commerce, media, and information ecosystems, studies of this kind serve a dual purpose: they hand attackers a sharper tool, but they also hand defenders a clearer map of where the walls are thin. The eight percent gain in attack success reported on Gowalla is not merely a benchmark increment; it is a quantified measure of how much behavioral data alone can be trusted, and a reminder that robustness against adversarial manipulation must be designed into recommendation systems from the ground up rather than bolted on after the fact.

Subject of Research: Gradient-penalized transferable adversarial attacks on recommendation systems

Article Title: Recgp: gradient penalization for transferable adversarial attacks in recommendation systems

Article References: Wu, C., Chen, H., Song, S., Yan, Y., Zhao, S., & Qian, F. (2026). Recgp: gradient penalization for transferable adversarial attacks in recommendation systems. Data Mining and Knowledge Discovery, 40(5), Article 88. https://doi.org/10.1007/s10618-026-01253-4

Image Credits: AI Generated

DOI: 10.1007/s10618-026-01253-4

Keywords: recommendation systems, adversarial examples, gradient penalty, loss landscape, transferability, shilling attacks, collaborative filtering, data poisoning, black-box attack, MovieLens, Gowalla, machine learning security