Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution

TL;DR

This paper introduces a real-time deep learning recommendation system for exercise activities that incorporates expert feedback based on a novel marginal distance probability distribution, addressing the unique challenges of exercise recommendation.

Contribution

It proposes a new active learning approach using marginal distance probability distribution to determine when to query experts in exercise recommendation systems.

Findings

Improved recommendation accuracy with expert-in-the-loop active learning.

Derived the probability distribution of marginal distance for uncertainty estimation.

Demonstrated effectiveness on a mHealth dataset.

Abstract

Recommendation systems play an important role in today's digital world. They have found applications in various applications such as music platforms, e.g., Spotify, and movie streaming services, e.g., Netflix. Less research effort has been devoted to physical exercise recommendation systems. Sedentary lifestyles have become the major driver of several diseases as well as healthcare costs. In this paper, we develop a recommendation system for daily exercise activities to users based on their history, profile and similar users. The developed recommendation system uses a deep recurrent neural network with user-profile attention and temporal attention mechanisms. Moreover, exercise recommendation systems are significantly different from streaming recommendation systems in that we are not able to collect click feedback from the participants in exercise recommendation systems. Thus, we propose a real-time, expert-in-the-loop active learning procedure. The active learners calculate the uncertainty of the recommender at each time step for each user and ask an expert for a recommendation when the certainty is low. In this paper, we derive the probability distribution function of marginal distance, and use it to determine when to ask experts for feedback. Our experimental results on a mHealth dataset show improved accuracy after incorporating the real-time active learner with the recommendation system.