deletions | additions       diff --git a/cc-intro.tex b/cc-intro.tex  index 7b0edfd..c704e98 100644  --- a/cc-intro.tex  +++ b/cc-intro.tex 
...
% As a general comment, we would say that this is largely how  % \emph{research and development} of recommender systems works, but  % without the same levels of system automony envisioned here.  \begin{table}%[htdp] \small  \begin{table}[ht]%dp]  \caption{Summary of case studies as viewed with our computational serendipity model}  \begin{center}  \begin{tabular}{|c|l|l|}  \hline  {\textbf Model} Model part}  & Evolutionary Music & Recommender \\  part & music  systems & Recommender  systems \\\hline  {\em Key Condition} && \\  Focus shift && \\  \hline  %{\em Key Condition} && \\  %* Focus shift && \\  %\hline  {\em Components} && \\  *  Serendipity trigger & &&  Input from user behaviour&  \\ *  Prepared mind & &  Through user model/domain model&  \\ *  Bridge & &  Elements identified outside clusters&  \\ *  Result & &  Dependent on organisation goals&  \\ \hline  {\em Dimensions} && \\  *  Chance && & & If learning from user behaviour  \\ *  Curiosity && & & Making unusual recommendations  \\ *  Sagacity && & & Updating models from user behaviour  \\ *  Value && & & As per business metrics/objectives  \\ \hline  {\em Environmental} && \\  {\em Factors} && \\  *  Dynamic world && As precondition for testing system's  \\ world && \hspace{3mm} influences on user behaviour\\  *  Multiple && User model and domain model\\  contexts && \\ *  Multiple && Making recommendations, learning\\  *  tasks && \hspace{3mm}from users, updating models  \\ *  Multipleinfluences  && Experimental design, psychology,  \\ influences && \hspace{3mm} domain understanding\\  \hline  \end{tabular}  \end{center}  \label{caseStudies}  \end{table}%  \normalsize