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arXiv · 1910.03040

IRF: Interactive Recommendation through Dialogue

Oznur Alkan·Massimiliano Mattetti·Elizabeth M. Daly·Adi Botea·Inge Vejsbjerg
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Abstract

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.

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Oznur Alkan, Massimiliano Mattetti, Elizabeth M. Daly, Adi Botea, Inge Vejsbjerg. 2019-10-03. IRF: Interactive Recommendation through Dialogue. https://arxiv.org/abs/1910.03040

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