Emotion aware recommendation engine using ECG signal analysis

Recommendation systems have been the backbone of the digital age, helping users choose relevant information from the pool of data. However, modern recommendation engines have an inherent flaw in the way they take user feedback. The implicit feedback, like history, can be misleading and might not provide the true sentiment of the user, and explicit feedback is incoherent. This paper aims to tackle this flaw by integrating user sentiment into recommender systems using ECG signals from a wearable device. Physiological signals like ECG have been known to contain the emotional information of the user. We propose a combination of classification and recommendation, wherein the AdaBoost classifier uses ECG signals from a wearable device for emotion classification into positive or negative feedback depending on the valence-arousal values, with an accuracy of 0.98. This feedback is then fed to the recommender engine as implicit feedback of the user giving precision and recall of 0.72 and 0.68, respectively. Therefore, the proposed approach allows for accurate and seamless implicit feedback that incorporates user emotions in recommendation systems.