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%\newpage
\section {Collaborative filtering}

- setup of the problem

- why is different

- examples : Amazon, Netflix, iTunes genius

\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.8\columnwidth]{collab_matrix} 
\vspace{-3ex}
\end{center}
\end{figure}	

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\newpage
\section{Pearson's Formula}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.8\columnwidth]{pearson} 
\vspace{-3ex}
\end{center}
\end{figure}	

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\newpage
\section{kNN for Collaborative filtering}




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\newpage
\section{Factorization, LSI}

\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.8\columnwidth]{LSI} 
\vspace{-3ex}
\end{center}
\end{figure}

LSI dimensions are\\
	- based on term co-occurrence patterns between documents (profiles)\\
	- ordered by their prominence in collection
\\
\\	
LSI space built from profiles\\
	- highlights common patterns among profiles\\
	- "noisy" dimensions can be pruned\\
	- project new documents into a collaborative space for routing\\

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\newpage
\section {Netflix and recent advances}

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\section {more examples}

- technical paper recommendations 

- librarian tasks


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%\section {6}