9 citations · 21 across the 26 of their papers we have counts for
4 papers · 1 filter
Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders
Kazuma Onishi, Katsuhiko Hayashi, Hidetaka Kamigaito
In real-world recommender systems, user-item interactions are Missing Not At Random (MNAR), as interactions with popular items are more frequently observed than those with less pop…
A Simple but Effective Closed-form Solution for Extreme Multi-label Learning
Kazuma Onishi, Katsuhiko Hayashi
Extreme multi-label learning (XML) is a task of assigning multiple labels from an extremely large set of labels to each data instance. Many current high-performance XML models are…
Implicit ZCA Whitening Effects of Linear Autoencoders for Recommendation
Katsuhiko Hayashi, Kazuma Onishi
Recently, in the field of recommendation systems, linear regression (autoencoder) models have been investigated as a way to learn item similarity. In this paper, we show a connecti…
Using Wikipedia Editor Information to Build High-performance Recommender Systems
Katsuhiko Hayashi
Wikipedia has high-quality articles on a variety of topics and has been used in diverse research areas. In this study, a method is presented for using Wikipedia's editor informatio…