\chapter{Background}

\section{Recommender Systems}

Recommender systems are software or technical facilities that provide items suggestions or predict individual preferences. These systems play an important role in commercial applications to increase sales and user satisfaction.

Recommender systems can be classified in two groups: Content-based systems and Collaborative filtering systems. 

\subsection{Content-based Recommender Systems}
Content-based recommender systems build an user profile by analysing user rated items. This profile is then processed to be correlated with another item to compute the interest of the user on this object. \cite{Lops11}

\subsection{Collaborative filtering Recommender System}
In collaborative filtering, recommendations are based on similarities between user's ratings. A model is built from a priori ratings to make predictions. \cite{Burke02}

\subsection{Hybrid Recommender Systems}
Hybrid recommendation is based on the combination of techniques mentioned above, by using the advantages of one system to compensate the disadvantages of the other system.
This project integrates item ratings from users and spectral features of audio and is based on a three-way aspect model \cite{Yoshii08}. Real item ratings are obtained through Last.fm API and spectral information are represented by convolutional deep belief networks (CDBN) features computed from items' spectrogram \cite{Lee09}.

\section{Online Social Networks}
Social network sites (SNSs) are “web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system”. \cite{JCC4:JCC4393}

\subsection{APIs}
The publicly available music related information can be collected from user profiles on social networks using Application Program Interface (API).

\section{Data Fusion Techniques}
Combination of multiple sources of information to obtain more relevant parameters is known as data fusion.
In this study, a cooperative data fusion technique is considered to augment information provided from social network source to content-based system features. \cite{Castanedo13}