1 code implementation • 22 Feb 2024 • 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) and their providers.
no code implementations • 15 Jun 2023 • Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka
In addition, ensuring that recommendation opportunities do not disproportionately favor popular users is essential for the total number of matches and for fairness among users.
1 code implementation • 8 Jun 2023 • Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura
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 effective handling of challenging samples within a dataset.
no code implementations • 23 May 2023 • Naoto Ohsaka, Riku Togashi
Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs.
no code implementations • 23 May 2023 • Naoto Ohsaka, Riku Togashi
Beyond accuracy, there are a variety of aspects to the quality of recommender systems, such as diversity, fairness, and robustness.
no code implementations • CVPR 2023 • Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh
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.
no code implementations • 9 Sep 2022 • Riku Togashi, Kenshi Abe
However, the intrinsic nature of fairness destroys the separability of optimisation subproblems for users and items, which is an essential property of conventional scalable algorithms, such as implicit alternating least squares (iALS).
no code implementations • 7 Sep 2022 • Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, Hitoshi Iyatomi
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce.
no code implementations • 24 Aug 2022 • Yoji Tomita, Riku Togashi, Daisuke Moriwaki
Online dating platforms provide people with the opportunity to find a partner.
no code implementations • CVPR 2022 • Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkila, Tetsuya Sakai
First, it is rank-insensitive: It ignores the rank positions of successfully localised moments in the top-$K$ ranked list by treating the list as a set.
1 code implementation • CVPR 2022 • Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh
OC-cost computes the cost of correcting detections to ground truths as a measure of accuracy.
no code implementations • 11 May 2021 • Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh
However, such methods have two main drawbacks particularly in large-scale applications; (1) the pairwise approach is severely inefficient due to the quadratic computational cost; and (2) even recent model-based samplers (e. g. IRGAN) cannot achieve practical efficiency due to the training of an extra model.
no code implementations • 19 Jan 2021 • Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones.
2 code implementations • 10 Nov 2020 • Riku Togashi, Mayu Otani, Shin'ichi Satoh
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items.
no code implementations • IJCNLP 2017 • Kaustubh Kulkarni, Riku Togashi, Hideyuki Maeda, Sumio Fujita
Embedding based approaches are shown to be effective for solving simple Question Answering (QA) problems in recent works.