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arXiv Machine Learning By Jutiyakhun Srisoparp, Donlapark Ponnoprat

Discovering Topics from Linear Autoencoders

Read the original on arXiv Machine Learning →

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.

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 Machine Learning.

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