# IRF: Interactive Recommendation through Dialogue

arXiv: https://arxiv.org/abs/1910.03040 · HTML: https://mlsanity.com/paper/1910.03040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent](https://mlsanity.com/paper/2506.14302/) (2025)

## Abstract (arXiv)

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.