Riku Togashi

According to our database, Riku Togashi authored at least 27 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

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

Riku Togashi, Kenshi Abe, Yuta Saito

CoRR, 2024

2023

Safe Collaborative Filtering.

Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura

CoRR, 2023

Exploration of Unranked Items in Safe Online Learning to Re-Rank.

Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

A Critical Reexamination of Intra-List Distance and Dispersion.

Naoto Ohsaka, Riku Togashi

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Curse of "Low" Dimensionality in Recommender Systems.

Naoto Ohsaka, Riku Togashi

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets.

Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

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

Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Fair Matrix Factorisation for Large-Scale Recommender Systems.

Riku Togashi, Kenshi Abe

CoRR, 2022

DM²S²: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention.

Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, Hitoshi Iyatomi

CoRR, 2022

DM²S²: Deep Multimodal Sequence Sets With Hierarchical Modality Attention.

Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, Hitoshi Iyatomi

IEEE Access, 2022

Matching Theory-based Recommender Systems in Online Dating.

Yoji Tomita, Riku Togashi, Daisuke Moriwaki

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval.

Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Tetsuya Sakai

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Optimal Correction Cost for Object Detection Evaluation.

Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Density-Ratio Based Personalised Ranking from Implicit Feedback.

Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh

Proceedings of the WWW '21: The Web Conference 2021, 2021

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

Riku Togashi, Mayu Otani, Shin'ichi Satoh

Proceedings of the WSDM '21, 2021

Scalable Personalised Item Ranking through Parametric Density Estimation.

Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh

Proceedings of the SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

2020

Visual Intents vs. Clicks, Likes, and Purchases in E-commerce.

Riku Togashi, Tetsuya Sakai

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

Automatic Evaluation of Iconic Image Retrieval based on Colour, Shape, and Texture.

Riku Togashi, Sumio Fujita, Tetsuya Sakai

Proceedings of the 2020 on International Conference on Multimedia Retrieval, 2020

2019

Closing the Gap Between Query and Database through Query Feature Transformation in C2C e-Commerce Visual Search.

Takuma Yamaguchi, Kosuke Arase, Riku Togashi, Shunya Ueta

Proceedings of the SIGIR 2019 Workshop on eCommerce, 2019

Generalising Kendall's Tau for Noisy and Incomplete Preference Judgements.

Riku Togashi, Tetsuya Sakai

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, 2019

Uplift Modeling for Cost Effective Coupon Marketing in C-to-C E-Commerce.

Akihiro Shimizu, Riku Togashi, Antony Lam, Nam Van Huynh

Proceedings of the 31st IEEE International Conference on Tools with Artificial Intelligence, 2019

2018

Classifying Community QA Questions That Contain an Image.

Kenta Tamaki, Riku Togashi, Sosuke Kato, Sumio Fujita, Hideyuki Maeda, Tetsuya Sakai

Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval, 2018

2017

Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017.

Vibhor Kanojia, Riku Togashi, Hideyuki Maeda

CoRR, 2017

Euclidean Image Embedding in view of Similarity Ranking in Auction Search by Image.

Riku Togashi, Hideyuki Maeda, Vibhor Kanojia, Kousuke Morimoto, Sumio Fujita

Proceedings of the 26th International Conference on World Wide Web Companion, 2017

Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling.

Vibhor Kanojia, Hideyuki Maeda, Riku Togashi, Sumio Fujita

Proceedings of the 26th International Conference on World Wide Web Companion, 2017

LSTM vs. BM25 for Open-domain QA: A Hands-on Comparison of Effectiveness and Efficiency.

Sosuke Kato, Riku Togashi, Hideyuki Maeda, Sumio Fujita, Tetsuya Sakai

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017

Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering.

Kaustubh Kulkarni, Riku Togashi, Hideyuki Maeda, Sumio Fujita

Proceedings of the Eighth International Joint Conference on Natural Language Processing, 2017