7 citations · 8 across the 6 of their papers we have counts for
6 papers
User Driven Model Adjustment via Boolean Rule Explanations
Elizabeth M. Daly, Massimiliano Mattetti, Öznur Alkan +1
AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landsca…
Client Network: An Interactive Model for Predicting New Clients
Massimiliano Mattetti, Akihiro Kishimoto, Adi Botea +4
Understanding prospective clients becomes increasingly important as companies aim to enlarge their market bases. Traditional approaches typically treat each client in isolation, ei…
User Profiling from Reviews for Accurate Time-Based Recommendations
Oznur Alkan, Elizabeth Daly
Recommender systems are a valuable way to engage users in a system, increase participation and show them resources they may not have found otherwise. One significant challenge is t…
IRF: Interactive Recommendation through Dialogue
Oznur Alkan, Massimiliano Mattetti, Elizabeth M. Daly +2
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and s…
An Evaluation Framework for Interactive Recommender System
Oznur Alkan, Elizabeth M. Daly, Adi Botea
Traditional recommender systems present a relatively static list of recommendations to a user where the feedback is typically limited to an accept/reject or a rating model. However…
Generating Dialogue Agents via Automated Planning
Adi Botea, Christian Muise, Shubham Agarwal +9
Dialogue systems have many applications such as customer support or question answering. Typically they have been limited to shallow single turn interactions. However more advanced…