# Trend-Based Categories Recommendations and Age-Gender Prediction for Pinterest and Twitter Users
## 4 Results Analysis
### Trends Analysis and Category Recommendations  
Our results for category recommendations are divided in two parts. First, we show the results from the pin classification when the category of the image is missing. Second, we discuss the recommendation results when a category is known. An accuracy of 42% was obtained for the Pinterest database DB1 with 32 categories. Further tests were performed for DB2 database, from where we obtained the rules described in Section 3, obtaining a 51% accuracy. Different metrics computed for classification performance are illustrated on Table 7. Note that metrics for the dataset DB2 are slightly different, we assume that this behavior is due to the unbalanced category of the dataset DB1 for some categories. In the second part of experimental tests, we used the DB1 dataset to test our category recommendation methods. We identified the users who posted images in at least five different categories and at the most 25. The number of users with this specific number of categories was 132 from the 256 users. Given a specific category from a user, a successful recommendation is made when a new category (recommended using method A or B illustrated in Table 3) is found in the list of categories for that user. The overall performance is obtained by calculating the percentage of successful recommendations for all of the posted categories per user, then averaging this percentage for all the 132 users. The first test was performed using only one category as a seed to look into the rules. For this test, the overall performance was 29% of successful recommendations for method A, and 30% for method B. The second test was performed using two categories taken as seed. Thus, for each user, all of the combinations of two categories were tested and validated with the rest of the posted categories of the \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Social Network** & **Class** & **Accuracy** & **F1** \\ \hline Logistic Regression ([PERSON] 2016 [20]) & Twitter PAN 2016 & Gender & 75\% & - \\  & (blogs \& reviews) & Age & 51\% & - \\ \hline SVM ([PERSON] 2018 [5]) & Twitter PAN 2014 & Gender & 75\% & - \\  & & Age & 40\% & - \\ \hline ResNet-50+KNN ([PERSON] 2019 [6]) & Pinterest & Gender & 74\% & 62\% \\ ResNet-50+LR) & & Age & 47\% & 39\% \\ \hline VGG16 ([PERSON] 2018 [19]) & Twitter PAN 2018 & Gender & 79\% & - \\ \hline Bag of Trees (Our approach) & Twitter PAN 2015 & Gender & 64\% & 64\% \\  & (DBT) & Age & 67\% & 59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Age and Gender prediction comparison with related approaches analyzing text from social network users. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Output** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
32 Categories (DB1) & 42\% & 42\% & 43\% & 42\% \\ \hline
32 Categories (DB2) & 51\% & 46\% & 50\% & 46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results for category classification on Pinterest images from two datasets. user. In this test, we achieved an overall performance of 19% with method A and 17% with method B. However, most of the systems that provide recommendations give more than one option to the user. To explore this possibility, we tested recommending more than one category to the user. In this sense, we considered a successful recommendation when at least one of them was in the posted categories. The results for 1, 2, and 3 suggestions are summarized in Table 8. As we expect, expanding the number of categories in the seed delivers a low percentage of successful recommendation, because it is more complicated to find a particular pair of categories than to find one in the item-set for method A or in the Base for the method B. Moreover, providing two or three suggested categories improves the probability of one of given suggestions can be chosen by user with an initial seed.