Accepted PAPERS

Evaluating the Explanation Tags of Online Food Delivery Recommendation: A Position Paper. Yurou Zhao, Ruidong Han, Fei Jiang, Lu Guan, Xiang Li, Wei Lin, Yiding Sun and Jiaxin Mao.

Investigating Users' Preferences for Explanation Tags on Online Delivery Platform. Yiding Sun, Yurou Zhao, Ruidong Han, Fei Jiang, Lu Guan, Xiang Li, Wei Lin and Jiaxin Mao.

Recommender Systems with Knowledge Graphs: Recency, Popularity, and Diversity of Explanations. Giacomo Balloccu, Ludovico Boratto, Gianni Fenu and Mirko Marras. Accepted at SIGIR'22.

Pairwise Review-Based Explanations for Voice Product Search. Gustavo Penha, Eyal Krikon and Vanessa Murdock. Accepted at CHIIR'22.

A Survey on Effects of Adding Explanations to Recommender Systems. Alexandra Vultureanu-Albisi and Costin Badica. Accepted in Concurrency and Computation: Practice and Experience, 2021.

Why trust technology? Combining legal and individuals’ perspectives to enable trust in news personalisation. Max van Drunen, Brahim Zarouali and Natali Helberger.

Towards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document SummarizationTowards Explainable AI: Assessing the Usefulness and Impact of Added Explainability Features in Legal Document Summarization. Milda Norkute, Nadja Herger, Leszek Michalak, Andrew Mulder and Sally Gao. Accepted as extended abstract at CHI'21.

Argumentative explanations for interactive recommendations. Antonio Rago, Oana Cocarascu, Christos Bechlivanidis, David Lagnado and Francesca Toni. Accepted in Artificial Intelligence, 2021.

The effect of explanations and algorithmic accuracy on visual recommender systems of artistic images. Denis Parra, Vicente Domínguez, Pablo Messina and Ivania Donoso-Guzmán. Accepted at IUI'19.

Automatic Generation of Natural Language Explanations. Aonghus Lawlor, Sixun Ouyang, Peter Dolog and Felipe Costa. Accepted at IUI'18 (companion proceedings).

Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design. Nicolas Mattis, Philipp Masur, Judith Moeller and Wouter van Atteveldt.

ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models. Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy and Gerhard Weikum. Accepted at TheWebConf'21.

Counterfactual Explanations for Neural Recommenders. Khanh Hiep Tran, Azin Ghazimatin and Rishiraj Saha Roy. Accepted at SIGIR'21.

Exploring the Role of Local and Global Explanations in Recommender Systems. Marissa Radensky, Doug Downey, Kyle Lo, Zoran Popović and Daniel Weld. Accepted as extended abstract at CHI'22.