Hands on Explainable Recommender Systems with Knowledge Graphs

Please note that an upgraded version of the tutorial has been presented for the ECIR24 conference and you can find it in this repository

Giacomo Balloccu Ludovico Boratto Gianni Fenu Mirko Marras
Department of Mathematics and Computer Science, University of Cagliari, ITALY

Hands on Tutorial on Explainable Recommender Systems with Knowledge Graphs held as part of the 16th ACM Conference on Recommender Systems, Seattle, WA, USA, 18th-23rd September 2022.

Material

• Website
• Video Recording
• Slides
• Drive Folder
• Notebook 1: Data, KG and Preprocessing
• Notebook 2: PGPR
• Notebook 3: CAFE
• Notebook 4: Evaluation and Explanation Generation

Citation

If the tutorial slides are useful for your research, we would appreciate an acknowledgment by citing our summary in the RecSys '22 proceedings:

@inproceedings{10.1145/3523227.3547374,
author = {Balloccu, Giacomo and Boratto, Ludovico and Fenu, Gianni and Marras, Mirko},
title = {Hands on Explainable Recommender Systems with Knowledge Graphs},
year = {2022},
isbn = {9781450392785},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3523227.3547374},
doi = {10.1145/3523227.3547374},
booktitle = {Proceedings of the 16th ACM Conference on Recommender Systems},
pages = {710–713},
numpages = {4},
keywords = {Explainability, Recommender Systems, Knowledge Graphs, Responsible Recommendation.},
location = {Seattle, WA, USA},
series = {RecSys '22}
}

If the tutorial notebooks are useful for your research, we would appreciate an acknowledgment by citing our SIGIR '22 paper:

@inproceedings{10.1145/3477495.3532041,
author = {Balloccu, Giacomo and Boratto, Ludovico and Fenu, Gianni and Marras, Mirko},
title = {Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3532041},
doi = {10.1145/3477495.3532041},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {646–656},
numpages = {11},
keywords = {knowledge graphs, recommender systems, explainability, fairness},
location = {Madrid, Spain},
series = {SIGIR '22}
}

@inproceedings{Balloccu2022ReinforcementRR,
  title={Reinforcement Recommendation Reasoning through Knowledge Graphs for Explanation Path Quality},
  author={Giacomo Balloccu and Ludovico Boratto and Gianni Fenu and Mirko Marras},
  year={2022}
}

Acknowledgements

This tutorial is produced with joint and equal effort of all the authors from the RecSys lab at University of Cagliari.

These notebooks are beginner-friendly and accessible according to Giacomo's HuggingFace Student Ambassador mission of democratising AI. Giacomo acknowledges HuggingFace 🤗 for their resources, people and support.

License

This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This software is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for details.

You should have received a copy of the GNU General Public License along with this source code. If not, go the following link: http://www.gnu.org/licenses/.