Hugging Face Trending Papers
Discovering Topics from Linear Autoencoders
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arXiv Machine Learning
Oct 28
Discovering Topics from Linear Autoencoders
The paper shows that latent topics can be extracted directly from the weight matrix of a closed‑form linear autoencoder under a latent‑factor model of user‑item interactions. These topics enable interpretation of recommendations without extra training, and the authors introduce item and user normalizations to mitigate dominance by popular items or active users. Experiments on six public datasets demonstrate that the method outperforms dedicated topic models in topic quality and matches or exceeds the best linear autoencoder, while maintaining competitive recommendation performance.
arXiv Machine Learning
1d ago
Alternatives to Shallow Autoencoders for Collaborative Filtering
arXiv:2611.06610v1 Announce Type: new Abstract: Collaborative filtering (CF) is a cornerstone of recommender systems and plays a relevant role in many modern applications. CF uses user-item interacti...
arXiv AI
Sep 12
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.
arXiv AI
Oct 28
Counterfactual Profile Perturbations for Recommendation Explanations: A Reproducibility Study
This reproducibility study investigates the counterfactual explanation method Learning to eXplain Recommendations (LXR) by extending its application beyond the top‑ranked item to other recommended items. The authors compare LXR against alternative baselines such as item popularity and latent factor similarity, noting that these baselines perform surprisingly well on certain tasks. They also analyze how item popularity affects explanation quality, finding that LXR produces shorter perturbations for less popular items, and conduct a brute‑force search to estimate a lower bound on optimal perturbation size.
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.
Hugging Face Trending Papers
Jul 23
Probabilistic Residual Learning for Online Recommendations
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.