An Automatic Ensemble Multi-Model Chat Context Music Recommender Model

Music has the ability to change the human's vibes,
emotions and moods. Most people have the habit of listening to
music while multitasking and travelling. In this article, focused
on developing a model to recommend music automatically based
on the text message sent in the chat applications. The propose an
Ensemble Multi-Model Chat Context Music Recommender,
which is influenced by BiLSTM Encoder to categories the user
chat text emotion, user personalized characteristic features using
Demographic information and the text emotion, personalized
traits and available music playlist using Integrated DeepCNN sub
models mapped to give immense user experience. The detailed
working and implementation results in 98.55% accuracy with
less loss ratio and performed better than other models for
WhatsApp-Chat dataset.