# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 4 Results and Discussion
### Efficiency Evaluation (RQ2)  
In this subsection, we evaluate the learning and decision efficiency of the proposed methods with the baseline methods. The length of each episode is also set to 32. We run each method on an AMD Ryzen 3600 6-core CPU with NVIDIA GeForce RTX2060 GPU. For the learning efficiency, we compare the average learning time per step for ATRec with three RL-based recommendation methods, i.e., DDPG-KNN, DQN-R, and ATRec, as shown in Table 3. Among the baseline methods, TPGR performs best as it can improve the learning efficiency at \(O(\log M)\). With the help of parameter-sharing strategies, ATRec can greatly reduce the parameters in learning and thus improving learning efficiencies at an average improvement of 35.5% on the three benchmark datasets. Specifically, the full-online \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Metric**} & \multirow{2}{*}{**Popularity**} & \multirow{2}{*}{**SVD**} & \multicolumn{2}{c}{**DDPG-**} & \multicolumn{2}{c}{**DDPG-**} & \multirow{2}{*}{**DQN-R**} & \multirow{2}{*}{**TPGR**} & \multirow{2}{*}{**ATRec**} & \multirow{2}{*}{**Improv.**} \\  & & & & **(K) = 1** & & **(\(k\) = 0.1N)** & & & & \\ \hline \multirow{8}{*}{Instant Video} & Reward@10 & 0.00004 & 0.00041 & 0.00264 & 0.01619 & 0.05082 & 0.11041 & **0.15478** & 40.2\% \\  & HR@10 & 0.00004 & 0.00119 & 0.00336 & 0.01807 & 0.05376 & 0.11782 & **0.17416** & 57.7\% \\  & MRR@10 & 0.00001 & 0.00041 & 0.00165 & 0.00679 & 0.02117 & 0.04569 & **0.08836** & 93.4\% \\ \cline{2-11}  & Reward@30 & 0.00012 & 0.003 & 0.00567 & 0.04598 & 0.11228 & 0.16860 & **0.24399** & 44.4\% \\  & HR@30 & 0.00016 & 0.00507 & 0.00713 & 0.05137 & 0.12150 & 0.18169 & **0.28041** & 54.3\% \\  & MRR@30 & 0.00002 & 0.00075 & 0.00190 & 0.00884 & 0.02497 & 0.05023 & **0.09431** & 87.8\% \\ \hline \multirow{8}{*}{Musical Instruments} & Reward@10 & 0.00008 & 0.00056 & 0 & 0.00004 & 0.03542 & 0.05270 & **0.05575** & 4.8\% \\  & HR@10 & 0.00010 & 0.00054 & 0 & 0.00005 & 0.03965 & 0.05998 & **0.06669** & 11.1\% \\  & MRR@10 & 0.00003 & 0.00011 & 0 & 0.00002 & 0.00506 & **0.03754** & 0.02262 & \(-\)39.7\% \\ \cline{2-11}  & Reward@30 & 0.00013 & 0.00332 & 0.00048 & 0.00004 & 0.06618 & 0.07350 & **0.09943** & 35.3\% \\ \cline{2-11}  & HR@30 & 0.00015 & 0.00471 & 0.00060 & 0.00005 & 0.07196 & 0.08056 & **0.12180** & 51.1\% \\ \cline{2-11}  & MRR@30 & 0.00004 & 0.00034 & 0.00004 & 0.00002 & 0.00747 & **0.03859** & 0.02590 & \(-\)32.9\% \\ \hline \multirow{8}{*}{Baby} & Reward@10 & 0 & 0.00373 & 0.00019 & 0.00208 & 0.04811 & **0.06859** & 0.06842 & \(-\)0.00\% \\  & HR@10 & 0 & 0.00054 & 0.00019 & 0.00216 & 0.05405 & 0.07514 & **0.08587** & 14.3\% \\ \cline{1-1}  & MRR@10 & 0 & 0.00086 & 0.00003 & 0.00057 & 0.01791 & 0.03523 & **0.03628** & 3.0\% \\ \cline{1-1} \cline{2-11}  & Reward@30 & 0.00001 & 0.00965 & 0.00019 & 0.00370 & 0.09720 & 0.12384 & **0.12885** & 4.0\% \\ \cline{1-1}  & HR@30 & 0.00003 & 0.01871 & 0.00019 & 0.00381 & 0.11064 & 0.13759 & **0.16719** & 21.5\% \\ \cline{1-1}  & MRR@30 & 0 & 0.00220 & 0.00003 & 0.00066 & 0.02142 & 0.03973 & **0.04311** & 9.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall performance comparison. algorithm ATRec needs less time in learning than TPGR, of which the state representation model is off-line trained. For decision efficiency, we specifically compare the two tree-based methods, i.e., ATRec and TPGR, and evaluate the average decision time per item and average decision time for top-K items. The results are shown in Table 4. For TPGR, the decision time for each item is faster than ATRec, as it just needs a transverse from root to leaf and only needs to calculate the nodes over the path, while ATRec needs to calculate all the output of the tree nodes. However, the decision time for calculating top-K items needs more time in TPGR, as ATRec can obtain all the recommendation probabilities for each item in a single computation, while TPGR needs to calculate the probabilities for each item iteratively.