Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering. - Kaustubh Kulkarni; Riku Togashi; Hideyuki Maeda; Sumio Fujita (IJCNLP(2), 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 (SIGIR, 2017)

Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling. - Vibhor Kanojia; Hideyuki Maeda; Riku Togashi; Sumio Fujita (WWW (Companion Volume), 2017)

Euclidean Image Embedding in view of Similarity Ranking in Auction Search by Image. - Riku Togashi; Hideyuki Maeda; Vibhor Kanojia; Kousuke Morimoto; Sumio Fujita (WWW (Companion Volume), 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)

Classifying Community QA Questions That Contain an Image. - Kenta Tamaki; Riku Togashi; Sosuke Kato; Sumio Fujita; Hideyuki Maeda; Tetsuya Sakai (ICTIR, 2018)

Uplift Modeling for Cost Effective Coupon Marketing in C-to-C E-Commerce. - Akihiro Shimizu; Riku Togashi; Antony Lam; Nam Van Huynh (ICTAI, 2019)

Generalising Kendall's Tau for Noisy and Incomplete Preference Judgements. - Riku Togashi; Tetsuya Sakai (ICTIR, 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 (eCOM@SIGIR, 2019)

Automatic Evaluation of Iconic Image Retrieval based on Colour, Shape, and Texture. - Riku Togashi; Sumio Fujita; Tetsuya Sakai (ICMR, 2020)

Visual Intents vs. Clicks, Likes, and Purchases in E-commerce. - Riku Togashi; Tetsuya Sakai (SIGIR, 2020)

Scalable Personalised Item Ranking through Parametric Density Estimation. - Riku Togashi; Masahiro Kato; Mayu Otani; Tetsuya Sakai; Shin'ichi Satoh 001 (SIGIR, 2021)

Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. - Riku Togashi; Mayu Otani; Shin'ichi Satoh 001 (WSDM, 2021)

Density-Ratio Based Personalised Ranking from Implicit Feedback. - Riku Togashi; Masahiro Kato; Mayu Otani; Shin'ichi Satoh 001 (WWW, 2021)

Optimal Correction Cost for Object Detection Evaluation. - Mayu Otani; Riku Togashi; Yuta Nakashima; Esa Rahtu; Janne Heikkilä; Shin'ichi Satoh 001 (CVPR, 2022)

AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval. - Riku Togashi; Mayu Otani; Yuta Nakashima; Esa Rahtu; Janne Heikkilä; Tetsuya Sakai (CVPR, 2022)

Matching Theory-based Recommender Systems in Online Dating. - Yoji Tomita; Riku Togashi; Daisuke Moriwaki (RecSys, 2022)

DM2S2: Deep Multimodal Sequence Sets With Hierarchical Modality Attention. - Shunsuke Kitada; Yuki Iwazaki; Riku Togashi; Hitoshi Iyatomi (IEEE Access, 2022)

DM2S2: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention. - Shunsuke Kitada; Yuki Iwazaki; Riku Togashi; Hitoshi Iyatomi (CoRR, 2022)

Fair Matrix Factorisation for Large-Scale Recommender Systems. - Riku Togashi; Kenshi Abe (CoRR, 2022)

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 001 (CVPR, 2023)

Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. - Yoji Tomita; Riku Togashi; Yuriko Hashizume; Naoto Ohsaka (RecSys, 2023)

Curse of "Low" Dimensionality in Recommender Systems. - Naoto Ohsaka; Riku Togashi (SIGIR, 2023)

A Critical Reexamination of Intra-List Distance and Dispersion. - Naoto Ohsaka; Riku Togashi (SIGIR, 2023)

Exploration of Unranked Items in Safe Online Learning to Re-Rank. - Hiroaki Shiino; Kaito Ariu; Kenshi Abe; Riku Togashi (SIGIR, 2023)

Robust Nearest Neighbors for Source-Free Domain Adaptation Under Class Distribution Shift. - Antonio Tejero-de-Pablos; Riku Togashi; Mayu Otani; Shin'ichi Satoh 001 (ECCV (72), 2024)

Safe Collaborative Filtering. - Riku Togashi; Tatsushi Oka; Naoto Ohsaka; Tetsuro Morimura (ICLR, 2024)

Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. - Riku Togashi; Kenshi Abe; Yuta Saito (WWW, 2024)

LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation. - Mayu Otani; Naoto Inoue; Kotaro Kikuchi; Riku Togashi (CoRR, 2024)

Instance-wise Supervision-level Optimization in Active Learning. - Shinnosuke Matsuo; Riku Togashi; Ryoma Bise; Seiichi Uchida; Masahiro Nomura (CVPR, 2025)