Gender and Interest Targeting for Sponsored Post Advertising at Tumblr
Table 3: Precision and recall of categorization methods
['[BOLD] Method', '[BOLD] Precision', '[BOLD] Recall']
To quantify the benefits of our approach, we evaluated the method by excluding random 1,000 tags from the editorially labeled set, and training the model using the remaining 7,400 labeled tags. We compared the SS-SG classification to the state-of-the-art logistic regression (LR) and k-NN, trained on the vectors learned by the original SG model. For LR classification (we refer to the method as LR-SG) we trained one classifier per interest category, while in k-NN (we refer to the method as k-NN-SG) for each test tag we found K=50 nearest categorized neighbors and predicted the category that appeared more than 10 times. The results indicate that classification based on our approach achieves higher precision than the competing methods, while at the same time maintaining competitive recall measure.