Publication
RecSys 2019
Conference paper

IRF: Interactive recommendation through dialogue

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Abstract

Recent research focuses beyond recommendation accuracy, towards human factors that infuence 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 refning recommendations based on learnt preferences.

Date

10 Sep 2019

Publication

RecSys 2019

Authors

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