Yukihiro Tagami

850 total citations · 1 hit paper
15 papers, 418 citations indexed

About

Yukihiro Tagami is a scholar working on Information Systems, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, Yukihiro Tagami has authored 15 papers receiving a total of 418 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Information Systems, 10 papers in Artificial Intelligence and 3 papers in Statistical and Nonlinear Physics. Recurrent topics in Yukihiro Tagami's work include Web Data Mining and Analysis (9 papers), Text and Document Classification Technologies (5 papers) and Recommender Systems and Techniques (4 papers). Yukihiro Tagami is often cited by papers focused on Web Data Mining and Analysis (9 papers), Text and Document Classification Technologies (5 papers) and Recommender Systems and Techniques (4 papers). Yukihiro Tagami collaborates with scholars based in Japan and United Kingdom. Yukihiro Tagami's co-authors include Akira Tajima, Shingo Ono, Koji Tsukamoto, Hayato Kobayashi, Nobuyuki Shimizu, Taiji Suzuki, Yuki Yano, Yusuke Tanaka and Tomoya Yamazaki and has published in prestigious journals such as IEICE Transactions on Information and Systems, Knowledge Discovery and Data Mining and Neural Information Processing Systems.

In The Last Decade

Yukihiro Tagami

12 papers receiving 383 citations

Hit Papers

Embedding-based News Reco... 2017 2026 2020 2023 2017 50 100 150 200 250

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yukihiro Tagami Japan 5 309 308 110 38 35 15 418
Zhiping Gu China 7 269 0.9× 379 1.2× 81 0.7× 86 2.3× 49 1.4× 9 413
Aghiles Salah Singapore 10 181 0.6× 210 0.7× 107 1.0× 42 1.1× 22 0.6× 14 305
Teng Xiao China 9 182 0.6× 176 0.6× 47 0.4× 66 1.7× 25 0.7× 17 259
Xuezhi Cao China 10 300 1.0× 241 0.8× 84 0.8× 71 1.9× 39 1.1× 21 408
Tong Man China 4 356 1.2× 289 0.9× 65 0.6× 62 1.6× 33 0.9× 6 424
Refuoe Mokhosi China 5 455 1.5× 540 1.8× 113 1.0× 166 4.4× 50 1.4× 9 602
Cosimo Palmisano Italy 5 77 0.2× 240 0.8× 92 0.8× 45 1.2× 49 1.4× 5 282
Diane Hu United States 8 164 0.5× 182 0.6× 63 0.6× 57 1.5× 19 0.5× 16 286

Countries citing papers authored by Yukihiro Tagami

Since Specialization
Citations

This map shows the geographic impact of Yukihiro Tagami's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yukihiro Tagami with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yukihiro Tagami more than expected).

Fields of papers citing papers by Yukihiro Tagami

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yukihiro Tagami. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yukihiro Tagami. The network helps show where Yukihiro Tagami may publish in the future.

Co-authorship network of co-authors of Yukihiro Tagami

This figure shows the co-authorship network connecting the top 25 collaborators of Yukihiro Tagami. A scholar is included among the top collaborators of Yukihiro Tagami based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yukihiro Tagami. Yukihiro Tagami is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Yamazaki, Tomoya, et al.. (2019). A Scalable and Plug-in Based System to Construct A Production-Level Knowledge Base.. Knowledge Discovery and Data Mining.
2.
Tagami, Yukihiro. (2019). Recursive Nearest Neighbor Graph Partitioning for Extreme Multi-Label Learning. IEICE Transactions on Information and Systems. E102.D(3). 579–587. 1 indexed citations
3.
Tagami, Yukihiro, Hayato Kobayashi, Shingo Ono, & Akira Tajima. (2018). Representation Learning for Users' Web Browsing Sequences. IEICE Transactions on Information and Systems. E101.D(7). 1870–1879. 1 indexed citations
4.
Tagami, Yukihiro. (2018). Speeding up Extreme Multi-Label Classifier by Approximate Nearest Neighbor Search. IEICE Transactions on Information and Systems. E101.D(11). 2784–2794. 1 indexed citations
5.
Tagami, Yukihiro, et al.. (2017). Embedding-based News Recommendation for Millions of Users. 1933–1942. 284 indexed citations breakdown →
6.
Tagami, Yukihiro. (2017). AnnexML. 455–464. 77 indexed citations
7.
Tagami, Yukihiro. (2017). Learning Extreme Multi-label Tree-classifier via Nearest Neighbor Graph Partitioning. 845–846. 4 indexed citations
8.
Suzuki, Taiji, et al.. (2016). Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning. Neural Information Processing Systems. 29. 3783–3791. 2 indexed citations
9.
Tagami, Yukihiro, et al.. (2016). Article De-duplication Using Distributed Representations. 87–88. 2 indexed citations
10.
Yano, Yuki, Yukihiro Tagami, & Akira Tajima. (2016). Quantifying Query Ambiguity with Topic Distributions. 1877–1880. 4 indexed citations
11.
Suzuki, Taiji, et al.. (2016). Gaussian process nonparametric tensor estimator and its minimax optimality. 1632–1641. 9 indexed citations
12.
Tagami, Yukihiro, Hayato Kobayashi, Shingo Ono, & Akira Tajima. (2015). Modeling User Activities on the Web using Paragraph Vector. 125–126. 6 indexed citations
13.
Tagami, Yukihiro, et al.. (2014). Translation method of contextual information into textual space of advertisements. 385–386. 1 indexed citations
14.
Tagami, Yukihiro, et al.. (2014). Filling context-ad vocabulary gaps with click logs. 1955–1964.
15.
Tagami, Yukihiro, et al.. (2013). CTR prediction for contextual advertising. 1–8. 26 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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