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ACM Transactions on Recommender Systems is a peer-reviewed journal published by ACM, covering Recommender Systems and Techniques, Topic Modeling, Advanced Graph Neural Networks. Over its lifetime it has published 211 papers, which have received 2,325 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 Recommender Systems 12, 3, Article 45 (2023).
Formats any DOI in ACM Transactions on Recommender Systems style. No sign-up. |
| Publishes research in | Recommender Systems and Techniques Topic Modeling Advanced Graph Neural Networks Advanced Bandit Algorithms Research Mobile Crowdsensing and Crowdsourcing |
| ISSN | 2770-6699 |
| Citation impact (2-yr) | 5.06 |
| h-index | 22 |
| i10-index | 49 |
| Top institutions publishing here | University of Klagenfurt |
| 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.
Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the literature on graph neural network-based recommender systems. We first introduce the background and the history of the development of…
Recommender systems are the algorithms which select, filter, and personalize content across many of the world's largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and…
Collaborative filtering (CF) is widely used to learn informative latent representations of users and items from observed interactions. Existing CF-based methods commonly adopt negative sampling to discriminate different items. That is, observed user-item pairs are treated as positive instances; unobserved pairs are considered as negative instances and are sampled under a defined distribution for training.…
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead to undesired effects on users, items, producers, platforms, or even the society at large, such as compromised user…
The field of recommender systems aims to predict users’ latent interests by analyzing their preferences and behaviors. However, privacy concerns about user data collection lead to challenges such as incomplete initial information and data sparsity. Federated learning has emerged to address these privacy issues in recommender systems. However, federated recommender systems face heterogeneity among edge…
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