## 5 Discussions  
From these experimental results, there are several observations. First, the performance of the CNN-based models (either text or image) is better than the baseline model (text). The improvement of TextCNN or ImageCNN over the baseline is 19% in terms of accuracy. This \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Number of** & **Time for Feature** & **Time for** & **Time for** & **Total Time** \\  & **Recommended Users** & **Extraction (ms)** & **Recommendation (ms)** & **Clustering (ms)** & **(ms)** \\ \hline Late fusion & 238 & 2,943,698 & 507,654 & (N/A) & 3,451,352 \\ Clustering & 238 & 2,943,698 & 177,318 & 1,342,126 & 4,463,124 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of late fusion and user clustering in terms of execution time. N/A, not applicable. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Dimension** & **Performance of Dataset\_1** & **Performance of Dataset\_2** \\ \hline Baseline\_text & 300 & 0.128 & 0.098 \\ Baseline\_pic & 2100 & 0.331 & 0.130 \\ TextCNN & 2100 & 0.326 & 0.172 \\ ImageCNN & 4096 & 0.327 & 0.173 \\ Early fusion & 2100 & 0.473 & 0.223 \\ Late fusion & 4096 & 0.491 & 0.281 \\ Autoencoder & 1500 & 0.462 & 0.245 \\ User clustering & 4096 & 0.367 & 0.207 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance comparison of all experimental results. shows that neural network-based models can improve the performance of content-based baseline models. However, in this paper, we focused on the task of user recommendation. Existing recommender systems focus on item recommendation on the basis of user ratings on different items. User recommendation is more challenging since we have many users, where each one has different attributes and preferences that cannot be exactly the same as others. When we categorize user preferences, multiclass classification is more difficult than binary classification. To the best of our knowledge, there is no publicly available dataset for user recommendation. Since there is no ground truth in real-world data, we assume that hashtags posted by users represent their implicit user preferences. For each user, the users with more similar hashtags are assumed to be the ground truth of their similar users. Then, we evaluate the performance by the average top-k accuracy of each system-generated user list, as shown in Equation (8). As shown in Section 4, all four proposed model variations outperformed the baseline model. Among the four different variations, late fusion achieved the best performance of 0.491 in terms of average top-k accuracy for similar user recommendation. This shows the effectiveness of the proposed model. Although autoencoders cannot further improve the accuracy, comparable performance can be obtained with the benefits of dimensionality reduction. This gives better efficiency for real-world recommendation systems. To further assess the performance on a larger scale, all models were executed on Dataset_2. We applied the same parameters as obtained in the best performing models in previous experiments for Dataset_2. From the experimental results, the best performance could be obtained with the average top-k accuracy of 0.281 for late fusion when we used the categories as the user feature with a dimension of 4096. It is worth noting that the proposed approach is far better than the baseline. The improvement across all models is about 8-18%. This further validates the usefulness of our proposed method in practice. The attributes in Dataset_2 are completely different from those in Dataset_1 since they were written in different languages. This shows the effectiveness of our proposed approach in different languages. The reasons why the performance of Dataset 2 was inferior to that of Dataset 1 are as follows: first, since more tags were used as our queries to Instagram to collect our Dataset 2, we could obtain more posts by more people which could give us more diverse content.