Counterfactual Explanations for Neural Recommenders

1 code implementation • 11 May 2021 • Khanh Hiep Tran, Azin Ghazimatin, Rishiraj Saha Roy

Understanding why specific items are recommended to users can significantly increase their trust and satisfaction in the system.

Collaborative Filtering counterfactual

ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

1 code implementation • 15 Feb 2021 • Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy, Gerhard Weikum

System-provided explanations for recommendations are an important component towards transparent and trustworthy AI.

PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems

1 code implementation • 19 Nov 2019 • Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, Gerhard Weikum

Interpretable explanations for recommender systems and other machine learning models are crucial to gain user trust.

counterfactual Recommendation Systems

FAIRY: A Framework for Understanding Relationships between Users' Actions and their Social Feeds

1 code implementation • 8 Aug 2019 • Azin Ghazimatin, Rishiraj Saha Roy, Gerhard Weikum

We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user.

Learning-To-Rank