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\chapter{Introduction}
Music has accompanied social activities on our daily lives and has influenced the shape of the technology landscape that we have today, such as portable media players, mobile device management applications and music stream services.

Recommender systems can be described as facilities that guide users to interesting objects in a huge space of information. In order to enhance  performance, there is the motivations of hybridization of two or more recommendation techniques.

This project is going to examine a different approach to develop a hybrid music recommender system in order to suggest new items that would be appealing and enjoyable to the users. This system will combine two recommendation techniques. The first technique is collaborative filtering to predict music preferences on the basis of users\' information from an online social network (OSN) such as Last.fm, and the second technique is content-based filtering in which acoustical features from audio tracks are correlated to compute their similarities.

Users' information will be obtained from the complementary Taste Profile subset, which is a part of the Million Song Dataset. The music library will be consolidated by crawling songs' information via 7digital API.

A convolutional neural network (CNN), which is a deep learning model, will be employed for describing the audio files of the music library. Estimation of distribution algorithms (EDA), which are optimization methods in statistics and machine learning, will be investigated to model user profiles that will be comparable with the features of the audio files to predict ratings and produce new item recommendations.

The evaluation of the hybrid recommender system will be assessed by prediction accuracy and performance comparison with a typical content-based system.

\section{Outline of the thesis}

The rest of the report is organised as follows:

\textbf{Chapter 2} reviews related work with deep learning techniques and Estimation of Distribution Algorithms on recommendation systems.

\textbf{Chapter 3} explains the proposed approach of the hybrid system for recommending new music items.

\textbf{Chapter 4} addresses the experiments and the evaluation scenarios of the performance for the hybrid recommender system.

\textbf{Chapter 5} discusses and analyses the results from the conducted experiments to evaluate the performance of the proposed hybrid music recommender system approach.

\textbf{Chapter 6} presents the conclusions and some thoughts for further research.