Filtering objectionable information access based on click-through behaviours with deep learning methods

This study explores URL click-through behaviour to predict the category of users' online information accesses and applies the results to progressively filter objectionable accesses during web surfing. Each clicked URL is represented by the embedding technique and fed into the Bidirectional Long Short-Term Memory neural network cascaded with a Conditional Random Field (BiLSTM-CRF) model to predict the category of a user's access. Large-scale experiments on click-through data from nearly one million real users show that our proposed BiLSTM-CRF model achieves promising results. The proposed method outperforms related approaches by a high accuracy of 0.9492 (near 27% relative improvement) for context-aware category prediction and an F1-score of 0.8995 (about 29% relative improvement) for objectionable access identification. In addition, in real-time filtering simulations, our model gradually achieves a macro-averaging blocking rate of 0.9221, while maintaining a favourably low false-positive rate of 0.0041.

Automated summary

This study uses the click-through behaviour of URLs to predict the category of users' online information accesses and applies the results to filter objectionable accesses during web surfing. The proposed BiLSTM-CRF model achieves promising results with high accuracy and F1-score improvements compared to related approaches. In real-time filtering simulations, the model maintains a high blocking rate and a low false-positive rate.