Julian McAuley
- Information Systems top 0.02%
- Recommender Systems and Techniques 62
- Artificial Intelligence top 0.05%
- Topic Modeling 70
- Advanced Graph Neural Networks 27
- Natural Language Processing Techniques 26
- Sentiment Analysis and Opinion Mining 13
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- Image Retrieval and Classification Techniques 14
- Multimodal Machine Learning Applications 13
- Computational Mathematics top 1%
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- Advanced Bandit Algorithms Research 22
- Co-authors
- Jure LeskovecWang-Cheng KangRuining HeAnton van den HengelQinfeng ShiJianmo NiJiacheng LiRahul Pandey
- Journals
- SHILAP Revista de lepidopterología (1 paper)Applied Physics Letters (1 paper)PLoS ONE (2 papers)
- Partner nations
- United StatesAustraliaChina
In The Last Decade
Julian McAuley
185 papers receiving 11.1k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Information Systems 6.3k
- Artificial Intelligence 7.2k
- Computer Vision and Pattern Recognition 3.0k
- Computational Mathematics 82
- Management Science and Operations Research 1.4k
Countries citing papers authored by Julian McAuley
This map shows the geographic impact of Julian McAuley'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 Julian McAuley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Julian McAuley more than expected).
Fields of papers citing papers by Julian McAuley
This network shows the impact of papers produced by Julian McAuley. 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 Julian McAuley. The network helps show where Julian McAuley may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Julian McAuley, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 20 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 7 | |
| 8 | 2024 | 23 | |
| 9 | 2024 | 0 | |
| 10 | 2023 | 59 | |
| 11 | 2023 | 25 | |
| 12 | 2023 | 3 | |
| 13 | 2023 | 9 | |
| 14 | 2023 | 2 | |
| 15 | 2022 | 24 | |
| 16 | 2022 | 5 | |
| 17 | 2022 | 7 | |
| 18 | Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspectsbreakdown → | 2019 | 626 |
| 19 | 2017 | 145 | |
| 20 | Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendationbreakdown → | 2016 | 401 |
About Julian McAuley
Julian McAuley is a scholar working on Artificial Intelligence, Health Informatics and Information Systems, having authored 203 papers that have together received 11.5k indexed citations. Recurring topics across this work include Topic Modeling (70 papers), Recommender Systems and Techniques (62 papers), Advanced Graph Neural Networks (27 papers), Natural Language Processing Techniques (26 papers), Advanced Bandit Algorithms Research (22 papers), Image Retrieval and Classification Techniques (14 papers), Sentiment Analysis and Opinion Mining (13 papers) and Multimodal Machine Learning Applications (13 papers). The work is most often cited by research in Information Systems (6.3k citations), Artificial Intelligence (7.2k citations) and Computer Vision and Pattern Recognition (3.0k citations). Julian McAuley has collaborated with scholars based in United States, Australia and China. Frequent co-authors include Jure Leskovec, Wang-Cheng Kang, Ruining He, Anton van den Hengel, Qinfeng Shi, Jianmo Ni, Jiacheng Li, Rahul Pandey, Mengting Wan and Tibério S. Caetano. Their work appears in journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.
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.