IRF: Interactive Recommendation through Dialogue
This work addresses the need for more interactive and transparent recommendation systems for users, though it appears incremental as it builds on existing non-interactive methods.
The paper tackles the problem of enhancing recommender systems with human-centric factors like user satisfaction and control by introducing a generic interactive framework that integrates dialogue systems to provide explanations, manage preferences, and refine recommendations.
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.