9 citations · 31 across the 9 of their papers we have counts for
8 papers
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Riku Togashi, Kenshi Abe, Yuta Saito
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video)…
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…
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…
Fair Matrix Factorisation for Large-Scale Recommender Systems
Riku Togashi, Kenshi Abe
Recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue in p…