Evaluating Content-based Pre-Training Strategies for a Knowledge-aware Recommender System based on Graph Neural Networks-Reference-Cited by-同舟云学术

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Evaluating Content-based Pre-Training Strategies for a Knowledge-aware Recommender System based on Graph Neural Networks

Published:2024-06-22 Issue: Volume: Page:165-171
ISSN:
Container-title:Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
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Author:

Spillo Giuseppe¹^ORCID,Bottalico Francesco²^ORCID,Musto Cataldo³^ORCID,De Gemmis Marco⁴^ORCID,Lops Pasquale⁵^ORCID,Semeraro Giovanni²^ORCID

Affiliation:

1. Computer Science, University of Bari, Italy

2. University of Bari, Italy

3. Department of Computer Science, Dipartimento di Informatica - University of Bari, Italy

4. Dept. of Computer Science, University of Bari Aldo Moro, Dept. of Computer Science, Italy

5. Department of Computer Science, University of Bari, Italy

Funder

PNRR project FAIR - Future AI Research (PE00000013)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3627043.3659548

Reference37 articles.

1. Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation

2. Knowledge-aware and conversational recommender systems

3. Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco M. Donini, and Tommaso Di Noia. 2021. Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation. CoRR abs/2103.02590 (2021).

4. How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs

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