# An Evaluation Framework for Interactive Recommender System

## TL;DR

This paper introduces a simulation-based evaluation framework for interactive recommender systems, enabling researchers to assess and tune their algorithms efficiently before conducting extensive user studies.

## Contribution

The authors propose a novel simulation framework with metrics to evaluate interactive recommenders, reducing reliance on time-consuming user studies and improving algorithm tuning.

## Key findings

- Framework allows simulation of user interactions
- Metrics evaluate recommendation quality post-simulation
- Facilitates pre-user study tuning of algorithms

## Abstract

Traditional recommender systems present a relatively static list of recommendations to a user where the feedback is typically limited to an accept/reject or a rating model. However, these simple modes of feedback may only provide limited insights as to why a user likes or dislikes an item and what aspects of the item the user has considered. Interactive recommender systems present an opportunity to engage the user in the process by allowing them to interact with the recommendations, provide feedback and impact the results in real-time. Evaluation of the impact of the user interaction typically requires an extensive user study which is time consuming and gives researchers limited opportunities to tune their solutions without having to conduct multiple rounds of user feedback. Additionally, user experience and design aspects can have a significant impact on the user feedback which may result in not necessarily assessing the quality of some of the underlying algorithmic decisions in the overall solution. As a result, we present an evaluation framework which aims to simulate the users interacting with the recommender. We formulate metrics to evaluate the quality of the interactive recommenders which are outputted by the framework once simulation is completed. While simulation along is not sufficient to evaluate a complete solution, the results can be useful to help researchers tune their solution before moving to the user study stage.