research-article

ClayRS: : An end-to-end framework for reproducible knowledge-aware recommender systems

Authors:

Marco Polignano,

Antonio Silletti,

Giovanni SemeraroAuthors Info & Claims

Volume 119, Issue C

https://doi.org/10.1016/j.is.2023.102273

Published: 01 October 2023 Publication History

Abstract

Knowledge-aware recommender systems represent one of the most innovative research directions in the area of recommender systems, aiming at giving meaning to information expressed in natural language and obtaining a deeper comprehension of the information conveyed by textual content.

Though rich and constantly evolving, the literature on knowledge-aware recommender systems is particularly scattered when considering software libraries. This makes it difficult to easily exploit advanced content representation and implement replicable experimental protocols. Accordingly, this work aims to fill in these gaps by introducing ClayRS, an end-to-end framework for replicable knowledge-aware recommender systems. ClayRS provides researchers and practitioners with the most recent state-of-the-art methodologies to build knowledge-aware content representations and also includes methods to exploit these representations in content-based recommendation algorithms. Finally, the structure of the framework also allows for building replicable pipelines to push forward the current research in the area and to develop accountable recommender systems.

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ClayRS: An end-to-end framework for reproducible knowledge-aware recommender systems

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Information Systems Volume 119, Issue C

Oct 2023

273 pages

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Cited By

Xu ZChu C(2024)Graph Convolution Network and User Interest Modeling for Enhanced Recommendation SystemsProceedings of the 2024 3rd International Conference on Cryptography, Network Security and Communication Technology10.1145/3673277.3673334(332-339)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3673277.3673334
Petruzzelli AMusto CLaraspata LRinaldi Ide Gemmis MLops PSemeraro G(2024)Instructing and Prompting Large Language Models for Explainable Cross-domain RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688137(298-308)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688137
Li JLi HHe ZMa WSun PZhang MMa S(2024)ReChorus2.0: A Modular and Task-Flexible Recommendation LibraryProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688076(454-464)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688076
Treuillier CCastagnos SÖzgöbek ÖBrun A(2024)Beyond Trade-offs: Unveiling Fairness-Constrained Diversity in News Recommender SystemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659571(143-148)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659571

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