This thesis focuses on 3 main tasks related to Document Recommendations. The first approach deals with applying existing techniques on Document Recommendations using Doc2Vec.
A robust representation of the same is presented to understand how noise induced in the embedding space affects predictions of the recommendations. The next phase focuses on improving the above recommendations using a Topic Classifier. A Hierarchical Attention Network is employed for this purpose. In order to increase the accuracy of prediction, this work establishes a relation to embedding size of the words in the article. In the last phase, model-agnostic Explainable AI (XAI) techniques are implemented to prove the findings in this thesis. XAI techniques are also employed to show how we can fine tune model hyper-parameters for a black-box model
