# Deep Learning-Based Context-Aware Recommender System Considering Contextual Features
## 4 Experiments
### Results of the Experiments  
In the development environment, the operating system was windows10, RAM was 16G, the language was Python, and the development tool was a Jupyter notebook. The following figure shows the experiment result to see the precision depending on learning rate. We use the Music, Restaurant, and Movie datasets. The results are shown in Figure 3. Although the best value is different for each dataset, most of them showed good results when the learning rate was 0.001. The following table shows the test results for the music, restaurant, and movie datasets obtained from the proposed method and the compared method. Tables 2 and 3 show the MAP and precision for the dataset obtained from the proposed method and the compared method. If the datasets are sorted in decreasing order of contextual dimensions, they are sorted as restaurant, movie and music. For the restaurant dataset, the non-contextual algorithm (i.e., in other words, the user KNN) yields similar results to those obtained from the proposed method. However, for the movie and music datasets, which have relatively higher contextual dimensions, the proposed method yields  
Figure 3: Precision on Music, Restaurant, and Movie datasets. much better performance than the user KNN. In addition, the proposed method for the restaurant dataset yields precision that are about 6% higher than the PMF. Similar to the user KNN, the proposed method yields better performance than the PMF for the movie and music datasets. For the restaurant dataset, the neural network-based method with deep learning technology and the proposed method yields better performance than other MF based methods, except for the PMF based method. The restaurant dataset has fewer contextual dimensions, so there is no significant difference in performance between the proposed method and other methods. However, the movie dataset has relatively higher contextual dimensions than the restaurant dataset, so for the movie dataset, the proposed method yields much better performance than other methods. Also, the music dataset with the highest contextual dimensions, shows much better performance than the other models. From this, we can conclude that for the dataset with the highest contextual dimensions, the proposed method yields much better performance than the existing methods. Finally, if the NN based method is compared to the proposed method, it seems that for every dataset, the proposed method yields higher precision and MAP. For the music dataset with high data sparsity, the proposed method yields about 17% higher precision than PMF's and shows about 4% higher MAP than ItemSplitting's. For the movie dataset, the proposed method yields about 5% higher precision than PMF's and shows about 2 times higher MAP than userKNN's. For the restaurant dataset, the proposed method yields about 6% higher precision than PMF's and shows about 8% higher MAP than ItemSplitting's. From these results, we can conclude that the proposed method is more effective than the context-aware recommender system in which deep learning-based techniques are applied. The proposed method shows better performance than the existing recommender system in datasets with high data sparsity, so it can be said to be a supplementary model for the problem of data sparsity. Also, considering that the accuracy of the recommender system is high, it can be seen that it is effective for nonlinear data by using deep learning. \begin{table}
\begin{tabular}{c c c c} \hline
**Precision@10** & **Music** & **Restaurant** & **Movie** \\ \hline UserKNN & 0.010082 & 0.062889 & 0.063732 \\ SVD++ & 0.045452 & 0.062174 & 0.059719 \\ CAMF\_CI & 0.024234 & 0.050423 & 0.058384 \\ ItemSplitting- & 0.045365 & 0.06249 & 0.054859 \\ BiasedMF & 0.003996 & 0.012763 & 0.004598 \\ CSLIM\_CI & 0.003996 & 0.012763 & 0.004598 \\ PMF & 0.048151 & 0.068362 & 0.06807 \\ FM & 0.010663 & 0.047136 & 0.027956 \\ NN based model & 0.050387 & 0.059913 & 0.076811 \\ Proposed model & **0.056760** & **0.072748** & **0.102263** \\ \hline \end{tabular}
\end{table}