\begin{table}[htttt]
  \vspace{-0.5cm}
  \centering
  \caption{Best parameterization for each approach.}
    \label{tbl:param}
    \begin{tabular}{|l|p{12cm}|}
    \hline
    \textbf{Approach}    & \textbf{Parameter}    \\\hline
    $LATRE$+$wTS$       & $\alpha=0.95$ for LastFM and MovieLens, $\alpha=0.9$ for Bibsonomy, YahooVideo and YouTube\\ \hline
    $RankSVM$         & linear kernel, $j=100$\\ \hline    $GP$          & $n=200$, $k=2$, $d=7$, $pc=0.6$ and $pm=0.1$\\\hline    $RankBoost$   & $i=500$ for Bibsonomy, $i=700$ for LastFM, i=$300$ for MovieLens, YahooVideo and YouTube\\\hline    $RF$          & $T=300$, $m=4$, $l=1000$ for Bibsonomy and LastFM, $l=300$ for the remaining datasets \\\hline    $MART$        & $l=5$, $lr=0.1$ and $i=1500$\\\hline    $\lambda$-$MART$ & $l=5$, $lr=0.1$ and  $i=1500$ \\\hline
    $AdaRank$     & $i=300$ for Bibsonomy, LastFM, MovieLens and YahooVideo,  $i=100$ for YouTube\\\hline
    $ListNet$     & $lr=0.00001$, $i=160$ for MovieLens, $i=10$ for YahooVideo, $i=40$ for Bibsonomy, LastFM and YouTube. \\\hline 

   \end{tabular}


 \end{table}