9 citations · 34 across the 18 of their papers we have counts for
18 papers
Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching
Ren Kishimoto, Rikiya Takehi, Koichi Tanaka +4
On two-sided matching platforms such as online dating and recruiting, recommendation algorithms often aim to maximize the total number of matches. However, this objective creates a…
Instance-wise Supervision-level Optimization in Active Learning
Shinnosuke Matsuo, Riku Togashi, Ryoma Bise +2
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness c…
LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation
Mayu Otani, Naoto Inoue, Kotaro Kikuchi +1
We introduce a layout similarity measure designed to evaluate the results of layout generation. While several similarity measures have been proposed in prior research, there has be…
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…
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…
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…
DMS: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention
Shunsuke Kitada, Yuki Iwazaki, Riku Togashi +1
There is increasing interest in the use of multimodal data in various web applications, such as digital advertising and e-commerce. Typical methods for extracting important informa…
Matching Theory-based Recommender Systems in Online Dating
Yoji Tomita, Riku Togashi, Daisuke Moriwaki
Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users.…
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval
Riku Togashi, Mayu Otani, Yuta Nakashima +3
Evaluation measures have a crucial impact on the direction of research. Therefore, it is of utmost importance to develop appropriate and reliable evaluation measures for new applic…
Optimal Correction Cost for Object Detection Evaluation
Mayu Otani, Riku Togashi, Yuta Nakashima +3
Mean Average Precision (mAP) is the primary evaluation measure for object detection. Although object detection has a broad range of applications, mAP evaluates detectors in terms o…
Scalable Personalised Item Ranking through Parametric Density Estimation
Riku Togashi, Masahiro Kato, Mayu Otani +2
Learning from implicit feedback is challenging because of the difficult nature of the one-class problem: we can observe only positive examples. Most conventional methods use a pair…
Density-Ratio Based Personalised Ranking from Implicit Feedback
Riku Togashi, Masahiro Kato, Mayu Otani +1
Learning from implicit user feedback is challenging as we can only observe positive samples but never access negative ones. Most conventional methods cope with this issue by adopti…
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
Riku Togashi, Mayu Otani, Shin'ichi Satoh
Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also…
Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017
Vibhor Kanojia, Riku Togashi, Hideyuki Maeda
Collaborative Knowledge Bases such as Freebase and Wikidata mention multiple professions and nationalities for a particular entity. The goal of the WSDM Cup 2017 Triplet Scoring Ch…