Wide and Deep Model

Deep Learning for Music Recommendation


In order to handle the challenge of both memorization and generalization, we introduce a new method called contented-based DNN (deep neural network) with transfer learning and pseudo-MF (matrix factorization). We apply our model to a Kaggle challenge on music recommendation and present a better result than using wide and deep model with Tensorflow APIs under these particular problem settings. We also analyze the performance of our model with a variety of other models to show the advantages and limitations of our model and propose potential work for futureresearchers

Columbia University


Visit my Github Repo for code and here for a final report