# A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and Images
## 5 Experimental Results  
### Performance Comparison to Baseline Models (RQ 1)  
We compare the recommendation performance of the proposed CAMRec model with the baseline models. The results from Table 2 indicate that the CAMRec outperforms the baseline models in all datasets. Next, we provide insights into three different aspects.  
First, the models using rating as the sole information (e.g., PMF and NeuMF) show the lowest recommendation performance among all baseline models. Such results indicate that a recommendation method using only rating information has limitations in capturing the specific purchase motives of users. However, since NeuMF leverages the nonlinearity of deep neural networks, it can model complex interactions between users and items that linear models like PMF cannot.  
Second, models using online reviews (e.g., DeepCoNN, RSBM, VBPR, and UCAM) show better performance than models using only the rating information. The results confirm the improvement in recommendation performance as online reviews contain more information on users' preferences for a specific item. Meanwhile, among the models using review text information, RSBM outperforms DeepCoNN. RSBM uses the target user's review text of an item, while DeepCoNN uses all review texts to learn the user's latent expressions that overlook the evaluation of the item varies with the user. Therefore, the results suggest that learning the target user's review text is more effective for personalized recommendations.  
Third, the multimodal model such as UCAM shows better recommendation performance than the models using a single source of information. Multimodal models can minimize the information loss by utilizing both texts and images as the sources of information. Therefore, such results suggest simultaneously using textual and visual information can minimize information loss and ensure accurate recommendations.  
Finally, our proposed CAMRec model show the best recommendation performance out of all baseline models for the following reasons: (1) Unlike PMF and NeuMF models using rating information to capture the interaction between users and items, the CAMRec model enhances the recommendation performance using various preference representations inherently found in online reviews. (2) Unlike DeepCoNN, RSBM, and VBPR models that incorporate a single modality, the proposed CAMRec model uses both textual and visual representations to learn. Therefore, the CAMRec model can provide recommendations through a more accurate reflection of diverse user preferences that a single modality may not capture. (3) Unlike the UCAM model which simply concatenates images and texts, the CAMRec model applies a co-attention mechanism to model the complementarity between the two modalities. Therefore, its recommendation performance is expected to improve as the model introduces the complementary and substitutive effects of texts and images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Cell Phones and Accessories**} & \multicolumn{2}{c}{**Electronics**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline PMF & 1.839 & 2.093 & 1.795 & 2.183 \\ NeuMF & 1.543 & 1.675 & 1.299 & 1.529 \\ DeepCoNN & 0.731 & 0.971 & 0.657 & 0.896 \\ RSBM & 0.566 & 0.830 & 0.525 & 0.783 \\ VBPR & 1.490 & 1.637 & 1.258 & 1.475 \\ UCAM & 0.516 & 0.804 & 0.463 & 0.790 \\
**CAMRec** & **0.460** & **0.725** & **0.417** & **0.701** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison with baseline models.