no code implementations • 28 Mar 2022 • Elizabeth M. Daly, Massimiliano Mattetti, Öznur Alkan, Rahul Nair
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 landscape or may be missing business logic.
no code implementations • 4 Jan 2022 • Öznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth M. Daly, Diptikalyan Saha
However, in such scenarios, it may take time for sufficient training data to accumulate in order to retrain the model to reflect the new decision boundaries.
no code implementations • 3 Oct 2019 • Oznur Alkan, Massimiliano Mattetti, Elizabeth M. Daly, Adi Botea, Inge Vejsbjerg
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.