MoodX

Mood X is developed using an artificial intellgence backbone that is based on a multi-tasking setup that integrates facial action unit detection to get a holisitic analysis of user mood that is reinforced with audio analysis of user voice usng an ensemble framework. Facial Action Units are a framework for facial expression annotation that are used to detect clusters of facial muscles that accoriding to literature, enables more confident models for facial expression analysis as all emotions are expressed by the activation of clusters of facial muscles that act in conjunction with each other.

Ensemble learning by using approaches such as bagging and boosting allow models trained under different circumstances to be combined to provide a final model that is several times stronger than any of the individual models. The final model possesses much better generalization capabilities. The audio branch of the network will use ensembles of support vector machines and attention networks. The final decision for recommendations are suggested using a confidence interval over the different ensembles.

Mood X will enable internet based services to greatly enhance user user experience. Today, many internet based entertainment and lifestyle services provide a myriad of options to choose from which while having its advantages, often leaves users flustered and confused. On the other hand, often users don't know what they really want and count on recommendations which are often disappointing. Mood X stands to change all this by providing users exactly what they want. This will improve customer retention for platforms that use this service as well, improving profits and enhancing brand confidence and popularity.

References

1. https://harry-7.github.io/speech-emotion-recognition/html/ : Speech classifier to analyse emotional state from speech
2. https://github.com/abhijeet3922/FaceEmotion_ID : Facial Emotion Classifier
3. Guan, X., Li, C.T. and Guan, Y., Enhanced SVD (ESVD) for Collaborative Filtering. : Algorithm implemented to build the recommedation system
4. https://github.com/khanhnamle1994/movielens : Dataset for movie recommendation