arXiv:2412. 20802v3 Announce Type: replace-cross Abstract: Recommender systems are widely used in the digital landscape to match users with content fitting their preferences.
By Aurore Archimbaud, Andreas Alfons, Ines Wilms
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
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.
By Dong Li, Zhenming Liu, Ruoming Jin, Hao Zhou, Zhi Liu, Jing Gao, Bin Ren
arXiv:2606. 16973v1 Announce Type: cross Abstract: Incorporating textual reviews into a Recommender System has become a prominent strategy for enriching collaborative signals with semantic information.
By Eduardo Ferreira da Silva, Mayki dos Santos Oliveira, Joel Machado Pires Denis Dantas Boaventura, Frederico Ara\'ujo Dur\~ao
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
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