9 citations · 34 across the 18 of their papers we have counts for
5 papers · 1 filter
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets
Yoji Tomita, Riku Togashi, Yuriko Hashizume +1
In matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform. Unlike standard recommender system…
Safe Collaborative Filtering
Riku Togashi, Tatsushi Oka, Naoto Ohsaka +1
Excellent tail performance is crucial for modern machine learning tasks, such as algorithmic fairness, class imbalance, and risk-sensitive decision making, as it ensures the effect…
A Critical Reexamination of Intra-List Distance and Dispersion
Naoto Ohsaka, Riku Togashi
Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified…
Curse of "Low" Dimensionality in Recommender Systems
Naoto Ohsaka, Riku Togashi
Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness. We argue that many of the prevalent problems in…
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation
Mayu Otani, Riku Togashi, Yu Sawai +5
Human evaluation is critical for validating the performance of text-to-image generative models, as this highly cognitive process requires deep comprehension of text and images. How…