RecSys 2020 Session P2B: Evaluating and Explaining Recommendations

Session P2B: Evaluating and Explaining Recommendations
Session Chairs: Ludovico Boratto and Li Chen

Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance
by Mesut Kaya (TU Delft), Derek Bridge (Insight Centre for Data Analytics, University College Cork), Nana Tintarev (TU Delft)

What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
by Gustavo Penha (Delft University of Technology), Claudia Hauff (Delft University of Technology)

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems
by Jin Huang (University of Amsterdam), Harrie Oosterhuis (University of Amsterdam), Maarten de Rijke (University of Amsterdam & Ahold Delhaize), Herke van Hoof (University of Amsterdam)

Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
by Darius Afchar (Deezer Research), Romain Hennequin (Deezer Research)

On Target Item Sampling in Offline Recommender System Evaluation
by Rocío Cañamares (Universidad Autónoma de Madrid), Pablo Castells (Universidad Autónoma de Madrid)

Recommendations as Graph Explorations
by Marialena Kyriakidi (University of Athens and Athena Research Center), Georgia Koutrika (Athena Research Center), Yannis Ioannidis (University of Athens and Athena Research Center)

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