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ORCIDRiku Togashi
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2020 – today
- 2024
[c24]Antonio Tejero-de-Pablos, Riku Togashi, Mayu Otani, Shin'ichi Satoh:
Robust Nearest Neighbors for Source-Free Domain Adaptation Under Class Distribution Shift. ECCV (72) 2024: 1-17- [c23]Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura:
Safe Collaborative Filtering. ICLR 2024 - [c22]Riku Togashi
, Kenshi Abe, Yuta Saito:
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. WWW 2024: 3307-3318 - [i17]Riku Togashi, Kenshi Abe, Yuta Saito:
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. CoRR abs/2402.14369 (2024) - [i16]Mayu Otani, Naoto Inoue, Kotaro Kikuchi, Riku Togashi:
LTSim: Layout Transportation-based Similarity Measure for Evaluating Layout Generation. CoRR abs/2407.12356 (2024) - 2023
- [c21]Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh:
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation. CVPR 2023: 14277-14286 - [c20]Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka:
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. RecSys 2023: 12-23 - [c19]Naoto Ohsaka, Riku Togashi:
Curse of "Low" Dimensionality in Recommender Systems. SIGIR 2023: 537-547 - [c18]Naoto Ohsaka, Riku Togashi:
A Critical Reexamination of Intra-List Distance and Dispersion. SIGIR 2023: 1619-1628 - [c17]Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi:
Exploration of Unranked Items in Safe Online Learning to Re-Rank. SIGIR 2023: 1991-1995 - [i15]Mayu Otani, Riku Togashi, Yu Sawai, Ryosuke Ishigami, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh:
Toward Verifiable and Reproducible Human Evaluation for Text-to-Image Generation. CoRR abs/2304.01816 (2023) - [i14]Hiroaki Shiino, Kaito Ariu, Kenshi Abe, Riku Togashi:
Exploration of Unranked Items in Safe Online Learning to Re-Rank. CoRR abs/2305.01202 (2023) - [i13]Naoto Ohsaka, Riku Togashi:
Curse of "Low" Dimensionality in Recommender Systems. CoRR abs/2305.13597 (2023) - [i12]Naoto Ohsaka, Riku Togashi:
A Critical Reexamination of Intra-List Distance and Dispersion. CoRR abs/2305.13801 (2023) - [i11]Riku Togashi, Tatsushi Oka, Naoto Ohsaka, Tetsuro Morimura:
Safe Collaborative Filtering. CoRR abs/2306.05292 (2023) - [i10]Yoji Tomita, Riku Togashi, Yuriko Hashizume, Naoto Ohsaka:
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets. CoRR abs/2306.09060 (2023) - 2022
- [j1]Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, Hitoshi Iyatomi:
DM2S2: Deep Multimodal Sequence Sets With Hierarchical Modality Attention. IEEE Access 10: 120023-120034 (2022) - [c16]Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Tetsuya Sakai:
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval. CVPR 2022: 21044-21053 - [c15]Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh:
Optimal Correction Cost for Object Detection Evaluation. CVPR 2022: 21075-21083 - [c14]Yoji Tomita, Riku Togashi, Daisuke Moriwaki:
Matching Theory-based Recommender Systems in Online Dating. RecSys 2022: 538-541 - [i9]Mayu Otani, Riku Togashi, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Shin'ichi Satoh:
Optimal Correction Cost for Object Detection Evaluation. CoRR abs/2203.14438 (2022) - [i8]Riku Togashi, Mayu Otani, Yuta Nakashima, Esa Rahtu, Janne Heikkilä, Tetsuya Sakai:
AxIoU: An Axiomatically Justified Measure for Video Moment Retrieval. CoRR abs/2203.16062 (2022) - [i7]Yoji Tomita, Riku Togashi, Daisuke Moriwaki:
Matching Theory-based Recommender Systems in Online Dating. CoRR abs/2208.11384 (2022) - [i6]Shunsuke Kitada, Yuki Iwazaki, Riku Togashi, Hitoshi Iyatomi:
DM2S2: Deep Multi-Modal Sequence Sets with Hierarchical Modality Attention. CoRR abs/2209.03126 (2022) - [i5]Riku Togashi, Kenshi Abe:
Fair Matrix Factorisation for Large-Scale Recommender Systems. CoRR abs/2209.04394 (2022) - 2021
- [c13]Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh:
Scalable Personalised Item Ranking through Parametric Density Estimation. SIGIR 2021: 921-931 - [c12]Riku Togashi, Mayu Otani, Shin'ichi Satoh:
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. WSDM 2021: 931-939 - [c11]Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh:
Density-Ratio Based Personalised Ranking from Implicit Feedback. WWW 2021: 3221-3233 - [i4]Riku Togashi, Masahiro Kato, Mayu Otani, Shin'ichi Satoh:
Density-Ratio Based Personalised Ranking from Implicit Feedback. CoRR abs/2101.07481 (2021) - [i3]Riku Togashi, Masahiro Kato, Mayu Otani, Tetsuya Sakai, Shin'ichi Satoh:
Scalable Personalised Item Ranking through Parametric Density Estimation. CoRR abs/2105.04769 (2021) - 2020
- [c10]Riku Togashi, Sumio Fujita, Tetsuya Sakai:
Automatic Evaluation of Iconic Image Retrieval based on Colour, Shape, and Texture. ICMR 2020: 346-354 - [c9]Riku Togashi, Tetsuya Sakai:
Visual Intents vs. Clicks, Likes, and Purchases in E-commerce. SIGIR 2020: 1869-1872 - [i2]Riku Togashi, Mayu Otani, Shin'ichi Satoh:
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph. CoRR abs/2011.05061 (2020)
2010 – 2019
- 2019
- [c8]Akihiro Shimizu, Riku Togashi, Antony Lam, Nam Van Huynh:
Uplift Modeling for Cost Effective Coupon Marketing in C-to-C E-Commerce. ICTAI 2019: 1744-1748 - [c7]Riku Togashi, Tetsuya Sakai:
Generalising Kendall's Tau for Noisy and Incomplete Preference Judgements. ICTIR 2019: 193-196 - [c6]Takuma Yamaguchi, Kosuke Arase, Riku Togashi, Shunya Ueta:
Closing the Gap Between Query and Database through Query Feature Transformation in C2C e-Commerce Visual Search. eCOM@SIGIR 2019 - 2018
- [c5]Kenta Tamaki, Riku Togashi, Sosuke Kato, Sumio Fujita, Hideyuki Maeda, Tetsuya Sakai:
Classifying Community QA Questions That Contain an Image. ICTIR 2018: 219-222 - 2017
- [c4]Kaustubh Kulkarni, Riku Togashi, Hideyuki Maeda, Sumio Fujita:
Dual Constrained Question Embeddings with Relational Knowledge Bases for Simple Question Answering. IJCNLP(2) 2017: 217-221 - [c3]Sosuke Kato, Riku Togashi, Hideyuki Maeda, Sumio Fujita, Tetsuya Sakai:
LSTM vs. BM25 for Open-domain QA: A Hands-on Comparison of Effectiveness and Efficiency. SIGIR 2017: 1309-1312 - [c2]Vibhor Kanojia, Hideyuki Maeda, Riku Togashi, Sumio Fujita:
Enhancing Knowledge Graph Embedding with Probabilistic Negative Sampling. WWW (Companion Volume) 2017: 801-802 - [c1]Riku Togashi, Hideyuki Maeda, Vibhor Kanojia, Kousuke Morimoto, Sumio Fujita:
Euclidean Image Embedding in view of Similarity Ranking in Auction Search by Image. WWW (Companion Volume) 2017: 847-848 - [i1]Vibhor Kanojia, Riku Togashi, Hideyuki Maeda:
Relevance Score of Triplets Using Knowledge Graph Embedding - The Pigweed Triple Scorer at WSDM Cup 2017. CoRR abs/1712.08353 (2017)
Coauthor Index
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last updated on 2024-11-11 21:27 CET by the dblp team
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