Abstract:
Recommendation systems are widely used in all domains. It is addressed in many ways. Which
includes content-based filtering, Collaborative filtering and combination of content based
filtering and collaborative filtering (Hybrid filtering) in Machine learning and neural network,
autoencoding, Convolutional networks in deep learning. Finding a solution for cold start
integrating adaptive contextual data using approach TCN application remains unresolved
because of the existing algorithms and its limited performance. Although various approaches in
Deep learning have provided multiple solutions, solutions found were not effective.
So, after multiple approaches have taken action to solve the problem, Temporal Convolutional
Network (TCN) seems most promising approach. So, it has been selected since well suited for
personalised music recommendation. Comparing to the approach Recurrent Neural Network
(RNN) and Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN) offer a
simple, scalable approach without relying on recurrent connections.
The system was tested by building a Deep learning (DL) model. The model was able to give an
output for music recommendations addressing cold start issue and according to adaptive
contextual data.
