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p@30: % CC BY-NC-SA 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/)
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p@30: %----------------------------------------------------------------------------------------
p@30: %	PACKAGES AND THEMES
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p@30: 
p@27: \documentclass{beamer}
p@27: 
p@30: \mode<presentation> {
p@27: 
p@30: % The Beamer class comes with a number of default slide themes
p@30: % which change the colors and layouts of slides. Below this is a list
p@30: % of all the themes, uncomment each in turn to see what they look like.
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p@30: % As well as themes, the Beamer class has a number of color themes
p@30: % for any slide theme. Uncomment each of these in turn to see how it
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p@30: %\setbeamertemplate{footline} % To remove the footer line in all slides uncomment this line
p@30: %\setbeamertemplate{footline}[page number] % To replace the footer line in all slides with a simple slide count uncomment this line
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p@30: \setbeamertemplate{navigation symbols}{} % To remove the navigation symbols from the bottom of all slides uncomment this line
p@30: }
p@30: 
p@30: \usepackage{graphicx} % Allows including images
p@30: \usepackage{booktabs} % Allows the use of \toprule, \midrule and \bottomrule in tables
p@30: 
p@30: %----------------------------------------------------------------------------------------
p@30: %	TITLE PAGE
p@30: %----------------------------------------------------------------------------------------
p@30: 
p@30: \title[Hybrid music recommender]{Hybrid music recommender using content-based and social information} % The short title appears at the bottom of every slide, the full title is only on the title page
p@30: 
p@30: \author{Paulo Esteban Chiliguano Torres} % Your name
p@30: \institute[QMUL] % Your institution as it will appear on the bottom of every slide, may be shorthand to save space
p@30: {School of Electrical Engineering and Computer Science\\
p@30: Queen Mary University of London \\ % Your institution for the title page
p@30: \medskip
p@30: %\textit{john@smith.com} % Your email address
p@30: }
p@30: \date{September 1st, 2015} % Date, can be changed to a custom date
p@27: 
p@27: \begin{document}
p@27: 
p@27: \begin{frame}
p@30: \titlepage % Print the title page as the first slide
p@27: \end{frame}
p@27: 
p@30: \begin{frame}
p@30: \frametitle{Outline} % Table of contents slide, comment this block out to remove it
p@30: \tableofcontents % Throughout your presentation, if you choose to use \section{} and \subsection{} commands, these will automatically be printed on this slide as an outline of your presentation
p@29: \end{frame}
p@27: 
p@30: %----------------------------------------------------------------------------------------
p@30: %	PRESENTATION SLIDES
p@30: %----------------------------------------------------------------------------------------
p@27: 
p@27: 
p@30: \section{Motivation}
p@30: \begin{frame}
p@30: 	\textit{''Music doesn't have any special meaning; it depends what it's attached to.''} (Oliver Sacks 1933-2015)
p@30: \end{frame}
p@30: \begin{frame}
p@30: \frametitle{Aim and Motivations}
p@30: Design and implement a hybrid music recommender to mitigate the cold-start problem in a content-based recommendation strategy. 
p@27: \begin{itemize}
p@30: \pause \item Implement a convolutional deep neural network (CDNN) to obtain high-level representation of an audio file.
p@30: \pause \item Investigate Estimation of Distribution Algorithms (EDAs) to model user profiles in terms of probabilities of music genres preferences.
p@30: \end{itemize}
p@30: \end{frame}
p@30: 
p@30: 
p@30: \subsection{Related work}
p@30: 
p@30: \begin{frame}
p@30: \frametitle{Recommender Systems}
p@30: Hybrid music recommender (Yoshii et al. 2008)
p@30: \begin{itemize}
p@30: 	\item ``bag of timbres'' to represent acoustic features.
p@30: 	\item Three-way aspect model: ``unobserved'' genre
p@30: \end{itemize}
p@30: \pause Deep content-based music recommendation (Oord et al. 2013)
p@30: \begin{itemize}
p@30: 	\item CDNN for latent vector representation
p@30: 	\item Million Song Dataset
p@30: \end{itemize}
p@30: \pause Hybrid recommender based on EDA (Liang, T. et al. 2014)
p@30: \begin{itemize}
p@30: 	\item TF-IDF for item attributes
p@30: 	\item Movielens dataset
p@30: 	\item Permutation EDA
p@27: \end{itemize}
p@27: 
p@27: 
p@27: 
p@30: %The following two theorems might be important to recall
p@30: %\begin{theorem}[Theorem 1]
p@30: %The HVG associated to a bi-infinite series of i.i.d. random variables extracted from a continuous probability distribution $f(x)$ is
p@30: %$P(k)=\bigg (\frac{1}{3}\bigg ) \bigg (\frac{2}{3}\bigg )^{k-2}; \ k=2,3,\dots \ \ \ \ \ \ (\forall f)$
p@30: %\end{theorem}
p@30: %\begin{theorem}[Theorem 2]
p@30: %\The DHVG associated to a bi-infinite series of i.i.d. random variables extracted from a continuous probability distribution $f(x)$ is
p@30: %$P(k)=\bigg (\frac{1}{2}\bigg )^k; \ k=1,2,3,\dots \ \ \ \ \ \ (\forall f)$
p@30: %\end{theorem}
p@27: \end{frame}
p@27: 
p@30: \section{Hybrid music recommendation}
p@30: \subsection{Design}
p@30: \begin{frame}
p@30: \frametitle{Hybrid music recommender design}
p@30: Fundamental tasks:
p@27: \begin{itemize}
p@30: 	\item User modelling
p@30: 	\item Information filtering
p@30: \end{itemize}
p@30: Required data:
p@30: \begin{itemize}
p@30: 	\item User-item matrix: Taste profile dataset (53 users)
p@30: 	\item Audio clips: 7digital UK catalogue (640 clips)
p@30: \end{itemize}
p@30: Song representation:
p@30: \begin{itemize}
p@30: 	\item 10-dimensional vector
p@30: 	\item Probability to belong to a music genre
p@27: \end{itemize}
p@27: 
p@27: 
p@30: %\begin{example}[Theorem Slide Code]
p@30: %Blablabla
p@30: %\end{example}
p@30: %And then you might be able to state the main conjecture you will solve
p@27: \end{frame}
p@27: 
p@30: \subsection{Architecture}
p@30: \begin{frame}
p@30: 	\frametitle{Hybrid music recommender approach}
p@30: 	\begin{itemize}
p@30: 		\item Feature augmentation
p@30: 		\item Meta-level
p@30: 	\end{itemize}
p@30: 	\begin{figure}[ht!]
p@30: 		\centering
p@30: 		\includegraphics[width=\textwidth]{hybrid.png}
p@30: 		%\caption{Diagram of the cleaning process of the Taste Profile subset}
p@30: 		%\label{fig:taste_profile}
p@30: 	\end{figure}
p@27: \end{frame}
p@27: 
p@30: \subsection{Item and user representation}
p@29: \begin{frame}
p@30: 	\frametitle{Probability of music genre}
p@30: 	%\begin{itemize}
p@30: 		%\item Feature augmentation
p@30: 		%\item Meta-level
p@30: 	%\end{itemize}
p@30: 	\begin{figure}[ht!]
p@30: 		\centering
p@30: 		\includegraphics[width=\textwidth]{CDNN.png}
p@30: 		\caption{CDNN for music genre classification (Kereliuk et al. 2015)}
p@30: 		%\label{fig:taste_profile}
p@30: 	\end{figure}
p@29: \end{frame}
p@29: 
p@30: \begin{frame}
p@30: 	\frametitle{Estimation of Distribution Algorithms (EDAs)}
p@30: 	\begin{figure}[ht!]
p@30: 		\centering
p@30: 		\includegraphics[width=0.5\textwidth]{eda.png}
p@30: 		\caption{Flowchart for EDA (Ding et al. 2015)}
p@30: 		%\label{fig:taste_profile}
p@30: 	\end{figure}
p@30: \end{frame}
p@30: \begin{frame}
p@30: 	\frametitle{User profile modelling}
p@30: 	With permutation EDA:
p@30: 	\begin{itemize}
p@30: 	\item 10 tags (GTZAN) equivalent to keywords
p@30: 	\item 50 weights: evenly spaced over the inverval $[0.1,\ldots,0.9]$
p@30: 	\end{itemize}
p@30: 	\frametitle{User profile modelling}
p@30: 	With continuous EDA:
p@30: 	\begin{itemize}
p@30: 		\item Each genre considered as a dimension
p@30: 		\item Compute mean and covariance for each dimension along individuals
p@30: 		\item Sample from normal distribution
p@30: 	\end{itemize}
p@30: \end{frame}
p@30: 
p@30: \section{Results}
p@30: \subsection{Music genre classifier}
p@30: \begin{frame}
p@30: 	\frametitle{Genre classification}
p@30: \begin{table}[h!]
p@30: 	\caption{Genre classification results} % title of Table
p@30: 	\centering % used for centering table
p@30: 	\begin{tabular}{c c c c c} % centered columns (4 columns)
p@30: 		\hline\hline %inserts double horizontal lines
p@30: 		Trial & Validation error (\%) & Test error (\%) & Iter. & Time elapsed (min.) \\ [0.5ex] % inserts table
p@30: 		%heading
p@30: 		\hline % inserts single horizontal line
p@30: 		1 & 58.0 & 65.2 & 650 & 7.00 \\ % inserting body of the table
p@30: 		2 & 37.6 & 46.0 & 2150 & 13.07 \\
p@30: 		3 & 39.6 & 46.0 & 700 & 7.54 \\
p@30: 		4 & 35.6 & 36.8 & 550 & 6.01 \\
p@30: 		5 & 36.4 & 40.0 & 250 & 5.47 \\
p@30: 		6 & 40.4 & 44.8 & 150 & 5.41 \\
p@30: 		7 & 32.4 & 40.4 & 800 & 8.64 \\
p@30: 		8 & 36.0 & 38.8 & 250 & 5.42 \\
p@30: 		9 & 34.0 & 38.8 & 850 & 9.14 \\ [1ex] % [1ex] adds vertical space
p@30: 		\hline %inserts single line
p@30: 	\end{tabular}
p@30: 	\label{table:genre} % is used to refer this table in the text
p@30: \end{table}
p@30: \end{frame}
p@30: 
p@30: \subsection{Hybrid recommender}
p@30: \begin{frame}
p@30: \frametitle{Top - N recommendation}
p@30: \begin{figure}[ht!]
p@30: 	\centering
p@30: 	\includegraphics[width=0.9\textwidth]{a.png}
p@30: 	%\caption{CDNN for music genre classification (Kereliuk et al. 2015)}
p@30: 	%\label{fig:taste_profile}
p@30: \end{figure}
p@30: \end{frame}
p@30: 
p@30: 
p@30: 
p@30: %------------------------------------------------
p@30: 
p@30: 
p@30: 
p@30: 
p@30: \section{Conclusions and future work}
p@30: \begin{frame}
p@30: \frametitle{Conclusions and future work}
p@30: \begin{itemize}
p@30: \item CDNN produce similar results to long-established music genre classifiers
p@30: \item Hybrid permutation EDA outperforms CB
p@30: \item Investigate unsupervised deep learning
p@30: \item Online evaluation
p@30: \end{itemize}
p@30: \end{frame}
p@30: 
p@30: 
p@30: 
p@30: %------------------------------------------------
p@30: 
p@30: \begin{frame}
p@30: \Huge{\centerline{Questions?}}
p@30: \end{frame}
p@30: 
p@30: %----------------------------------------------------------------------------------------
p@30: 
p@30: \end{document} 



