Deep Learning for Recommender Systems

Deep learning and recommender systems are current fiery research areas in this modern times. There are a great number of new rising techniques and developing models each year. With the ever-growing vastness of online materials, recommender systems have been an effectual approach to overcome such informative overload. The usefulness of recommender systems cannot be exaggerated, given its extensive adoption in many web applications, along with its effective impact to amend many problems associated to over-choice. Deep learning methods have become the mode of choice for scholars work- ing on algorithmic qualities of recommender systems. The effect of deep learning is also prevalent, recently establishing its effectiveness when used in information retrieval and recommender systems research. In this thesis, I cover the current advances created in the field of recommendation using vari- ous modifications of deep learning technology. Also, with a practical example in Neural Collaborative Filtering.