Search Results for author: Riku Togashi

Found 15 papers, 4 papers with code

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems

1 code implementation22 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.

Collaborative Filtering Exposure Fairness +1

Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets

no code implementations15 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.

Fairness Recommendation Systems

Safe Collaborative Filtering

1 code implementation8 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.

Collaborative Filtering Computational Efficiency +3

A Critical Reexamination of Intra-List Distance and Dispersion

no code implementations23 May 2023 Naoto Ohsaka, Riku Togashi

Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs.

Diversity

Curse of "Low" Dimensionality in Recommender Systems

no code implementations23 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.

Diversity Fairness +1

Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation

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.

Text-to-Image Generation

Fair Matrix Factorisation for Large-Scale Recommender Systems

no code implementations9 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).

Collaborative Filtering Fairness +1

DM$^2$S$^2$: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention

no code implementations7 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.

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval

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.

Moment Retrieval Retrieval

Scalable Personalised Item Ranking through Parametric Density Estimation

no code implementations11 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.

Density Estimation Learning-To-Rank

Density-Ratio Based Personalised Ranking from Implicit Feedback

no code implementations19 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.

Density Ratio Estimation

Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph

2 code implementations10 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.

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