A Counterfactual Framework for Learning and Evaluating Explanations for Recommender Systems
Author:
Affiliation:
1. The Open University, Ra'anana, Israel
2. Tel-Aviv University, Tel Aviv, Israel
3. Tel Aviv University, Tel Aviv, Israel
Funder
Israeli Science Foundation (ISF)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645560
Reference111 articles.
1. Explainable Matrix Factorization for Collaborative Filtering
2. Using Explainability for Constrained Matrix Factorization
3. Samira Abnar and Willem Zuidema. 2020. Quantifying Attention Flow in Transformers. arXiv preprint arXiv:2005.00928 (2020).
4. Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity checks for saliency maps. Advances in neural information processing systems , Vol. 31 (2018).
5. Openxai: Towards a transparent evaluation of model explanations;Agarwal Chirag;Advances in Neural Information Processing Systems,2022
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1. KPAR: Knowledge-aware Path-based Attentive Recommender with Interpretability;ACM Transactions on Recommender Systems;2024-06-17
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