The paper investigates why many state‑of‑the‑art recommendation algorithms, despite using diverse deep‑learning techniques, achieve similar performance. It shows that the key commonality is a regularizer: either a nuclear‑norm or a Frobenius‑norm term. The authors further propose two new low‑rank, closed‑form solutions that combine the advantages of both regularizers.
arXiv:2606. 01948v1 Announce Type: cross Abstract: The growing popularity of group activities has increased the need for methods that provide recommendations to groups of users given their individual preferences.
arXiv:2607. 22665v1 Announce Type: new Abstract: Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interaction data.
arXiv:2607. 24025v1 Announce Type: cross Abstract: Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields suboptimal performance, sometimes even trailing behind well-designed simple recommendation models.
arXiv:2210. 10619v3 Announce Type: replace-cross Abstract: Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence.
arXiv:2403. 00802v2 Announce Type: replace-cross Abstract: Production-grade recommender systems rely heavily on a large-scale corpus used by online media services, including Netflix, Pinterest, and Amazon.
arXiv:2607. 10910v1 Announce Type: cross Abstract: We present ZoRRO (Zero-Weight Personalized Recommender System), a zero-weight, training-free framework for personalized news recommendation designed for scalable real-world deployment.
arXiv:2607. 26832v1 Announce Type: new Abstract: Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world news has a short Time-to-Live (TTL < 48 h) and shallow article pools.
The article surveys graph foundation models (GFMs) for recommender systems, highlighting how they combine graph neural networks (GNNs) and large language models (LLMs) to better capture user-item relationships and textual data. It offers a taxonomy of current GFM approaches, discusses methodological details, and identifies key challenges and future research directions. The survey aims to provide comprehensive insights into the evolving landscape of GFM-based recommender systems.
arXiv:2603.02561v2 Announce Type: replace-cross Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic co...
arXiv:2607. 08754v1 Announce Type: cross Abstract: Low-rank factorization is widely used to compress neural networks, but modern models are often not naturally amenable to aggressive factorization without significant accuracy loss.
arXiv:2403. 10232v2 Announce Type: replace-cross Abstract: Conventional matrix completion methods approximate the missing values by assuming the matrix to be low-rank, which leads to a linear approximation of missing values.
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically enhance their recommendation capabilities.