arXiv Machine Learning
Alternatives to Shallow Autoencoders for Collaborative Filtering
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
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.
Hugging Face Trending Papers
Oct 26
Discovering Topics from Linear Autoencoders
Linear recommenders and topic models are similar in that they implicitly learn from co-occurrences of items. However, there has been little study of how these two model families are related. In this w...
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.
arXiv AI
Oct 27
Cross-Domain Sequential Recommendation with Representation-Level User Transfer
arXiv:2610.35550v1 Announce Type: cross Abstract: Cross-domain sequential recommendation aims to improve next-item prediction in a target domain by leveraging user behavior from auxiliary source doma...
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 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.