\begin{table}[h!]
  \vspace{-0.4cm}
%  \small
    \centering
    \caption{Average recommendation time (milliseconds) per object, divided in stages: Lazy Rule Generation (LRG), Metric Computation (MC), Model Application (MA) and Candidate Sorting (CS)}
    \label{tab:time}
    \begin{tabular}{ll|ccccc}

    ~          & & Bibsonomy          &  LastFM             & MovieLens         & YahooVideo               & YouTube          \\\hline
\multicolumn{2}{c|}{LRG}                  & 116.06  $\pm$  2.46 & 1198.11  $\pm$  72.97 & 238.00  $\pm$  12.10 & 968.18  $\pm$  84.77 & 592.13  $\pm$  100.71 \\
\hline
\multicolumn{2}{c|}{MC (L2R methods)}                   & 0.53  $\pm$  0.01 & 0.96  $\pm$  0.04 & 0.49  $\pm$  0.02 & 0.58  $\pm$  0.01 & 0.97  $\pm$  0.02 \\
\hline 
\multicolumn{2}{c|}{MC ($LATRE$+$wTS$)}   & 0.04  $\pm$  0.01 & 0.10  $\pm$  0.01 & 0.03  $\pm$  0.02 & 0.04  $\pm$  0.00 & 0.03  $\pm$  0.01 \\
\hline
 \multirow{9}{*}{MA}
 & $LATRE$+$wTS$     & 0.02  $\pm$  0.01 & 0.04  $\pm$  0.01 & 0.02  $\pm$  0.01 & 0.02  $\pm$  0.01 & 0.03  $\pm$  0.01 \\
 
 & $RankSVM$         & 0.72  $\pm$  0.04 & 1.52  $\pm$  0.07 & 0.46  $\pm$  0.00 & 0.65  $\pm$  0.08 & 1.35  $\pm$  0.06 \\
 & $GP$              & 2.12  $\pm$  0.13 & 5.35  $\pm$  0.53 & 2.23  $\pm$  0.23 & 1.95  $\pm$  0.12 & 4.99  $\pm$  0.56 \\
 & $RankBoost$       & 0.59  $\pm$  0.02 & 1.30  $\pm$  0.02 & 1.38  $\pm$  0.08 & 0.53  $\pm$  0.02 & 1.17  $\pm$  0.09 \\
 
 & $RF$              & 47.20  $\pm$  1.66 & 54.43  $\pm$  0.31 & 799.23   $\pm$  45.00 & 55.63   $\pm$  1.73 & 57.18  $\pm$  1.41 \\
 & $MART$            & 10.24  $\pm$  0.12 & 19.11  $\pm$  0.19 & 82.38    $\pm$  0.62 & 10.23    $\pm$  0.18 & 16.01  $\pm$  0.69 \\ 
 & $\lambda$-$MART$  & 50.76  $\pm$  9.83 & 60.69  $\pm$  32.13 & 472.00  $\pm$  119.92 & 52.29  $\pm$  9.66 & 90.02  $\pm$  35.64 \\
 
 & $AdaRank$         & 1.48  $\pm$  0.23 & 4.53  $\pm$  0.69 & 3.92  $\pm$  0.85 & 1.33  $\pm$  0.09 & 4.11  $\pm$  1.27 \\
 & $ListNet$         & 1.43  $\pm$  0.24 & 2.79  $\pm$  1.17 & 2.38  $\pm$  1.08 & 1.05  $\pm$  0.19 & 2.91  $\pm$  0.34 \\

\hline
\multicolumn{2}{c|}{CS}                   & 0.04  $\pm$  0.01 & 0.08  $\pm$  0.01 & 0.05  $\pm$  0.01 & 0.03  $\pm$  0.01 & 0.07  $\pm$  0.01 \\

 \hline
 
  \end{tabular}
  
  \end{table}
  
  
  
  
  \begin{table}[h!]
  \vspace{-0.3cm}
    \centering
    \caption{Average recommendation time (milliseconds) per object}
    \label{tab:total_time}
    
    \begin{tabular}{l|ccccc}
		
		    ~         & Bibsonomy          &  LastFM             & MovieLens         & YahooVideo               & YouTube          \\\hline
		    								
$LATRE$+$wTS$     & 116 $\pm$ 2 & 1198 $\pm$ 73 & 238 $\pm$ 12 & 968 $\pm$ 85 & 592 $\pm$ 101 \\
$RankSVM$         & 117 $\pm$ 3 & 1201 $\pm$ 73 & 239 $\pm$ 12 & 969 $\pm$ 85 & 595 $\pm$ 101 \\
$GP$              & 119 $\pm$ 3 & 1205 $\pm$ 74 & 241 $\pm$ 12 & 971 $\pm$ 85 & 598 $\pm$ 101 \\


$RankBoost$       & 117 $\pm$ 2 & 1200 $\pm$ 73 & 240 $\pm$ 12 & 969 $\pm$ 85 & 594 $\pm$ 101 \\
$RF$              & 164 $\pm$ 4 & 1254 $\pm$ 73 & 1038 $\pm$ 57 & 1024 $\pm$ 87 & 650 $\pm$ 102 \\
$MART$            & 127 $\pm$ 3 & 1218 $\pm$ 73 & 321 $\pm$ 13 & 979 $\pm$ 85 & 609 $\pm$ 101 \\
$\lambda$-$MART$  & 167 $\pm$ 12 & 1260 $\pm$ 105 & 711 $\pm$ 132 & 1021 $\pm$ 94 & 683 $\pm$ 136 \\

$AdaRank$         & 118 $\pm$ 3 & 1204 $\pm$ 74 & 242 $\pm$ 13 & 970 $\pm$ 85 & 597 $\pm$ 102 \\
$ListNet$         & 118 $\pm$ 3 & 1202 $\pm$ 74 & 241 $\pm$ 13 & 970 $\pm$ 85 & 596 $\pm$ 101 \\ 




\hline

 
  \end{tabular}
  
  
  \end{table}