arXiv AI September 12, 2026 By Dong Li, Zhenming Liu, Ruoming Jin, Hao Zhou, Zhi Liu, Jing Gao, Bin Ren

On the Regularization Landscape for the Linear Recommendation Models

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

benchmarks

arXiv AI

Jun 2

Rank-Constrained Deep Matrix Completion for Group Recommendation

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.

By Mubaraka Sani Ibrahim, Lehel Csat\'o, Isah Charles Saidu

llms diffusion efficiency

arXiv AI

Jul 28

Obliviate: Efficient Unlearning in Recommender Systems

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.

By Tushar Prakash, Brijraj Singh, Niranjan Pedanekar, Narayan Chaturvedi

efficiency safety

arXiv Machine Learning

Jul 28

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation

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.

By Yu Cui, Yi Xu, Jiahao Wang, Hao Zhang, Yu Zhang, Xiaoyi Zeng, Can Wang, Jinxin Hu, Jiawei Chen

llms rag benchmarks safety

arXiv AI

Jul 7

Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering

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.

By \'Angel Gonz\'alez-Prieto, Abraham Guti\'errez, Fernando Ortega, Ra\'ul Lara-Cabrera

arXiv AI

Aug 10

Towards a Theoretical Understanding of Two Tower Recommendation Models

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.

By Amit Kumar Jaiswal

rag

arXiv Machine Learning

Jul 14

ZoRRO: A Zero-Weight Personalized Recommender System for Scalable News Recommendation

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.

By Johannes Kruse, Ryotaro Shimizu, Kasper Lindskow, Jon Tofteskov, Michael Riis Andersen, Julian McAuley, Jes Frellsen

benchmarks

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