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ACM Transactions on Intelligent Systems and Technology is a peer-reviewed journal published by ACM, covering Topic Modeling, Human Mobility and Location-Based Analysis, Recommender Systems and Techniques. Over its lifetime it has published 1,505 papers, which have received 98,145 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. ACM Transactions on Intelligent Systems and Technology 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Intelligent Systems and Technology style. No sign-up. |
| Publishes research in | Topic Modeling Human Mobility and Location-Based Analysis Recommender Systems and Techniques Advanced Graph Neural Networks Privacy-Preserving Technologies in Data |
| ISSN | 2157-6904 |
| Citation impact (2-yr) | 14.13 |
| h-index | 99 |
| i10-index | 772 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | dl.acm.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM.…
Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce…
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding…
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article,…
Factorization approaches provide high accuracy in several important prediction problems, for example, recommender systems. However, applying factorization approaches to a new prediction problem is a nontrivial task and requires a lot of expert knowledge. Typically, a new model is developed, a learning algorithm is derived, and the approach has to be implemented. Factorization machines (FM)…
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