5 papers
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…
Multi-label Learning with Random Circular Vectors
Ken Nishida, Kojiro Machi, Kazuma Onishi +2
The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. Whi…
IRR: Image Review Ranking Framework for Evaluating Vision-Language Models
Kazuki Hayashi, Kazuma Onishi, Toma Suzuki +7
Large-scale Vision-Language Models (LVLMs) process both images and text, excelling in multimodal tasks such as image captioning and description generation. However, while these mod…
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…