LAUSR

User interaction prediction for Web applications is crucial to improve browsing experience. With the prediction, for instance, a browser can prefetch the content to be accessed next reducing long wait times in advance. However, predicting the user interaction is challenging in practice. Collecting detailed interaction data is difficult due to the constraints on target application and platform. Moreover, Web navigation prediction mechanisms for general applications have a low accuracy with conventional machine learning models. To this end, in this paper, we propose a Web interaction profiling framework, WebProfiler, which collects user interaction data in a generic way and accurately predicts the next navigation. Both navigation and click events are collected by using JavaScript event handlers and clicked objects are identified reliably through a document object model based approach. Furthermore, we adopt gated recurrent unit (GRU), a representative deep learning technique suitable for coping with time series Web interaction data, and present two advanced techniques for training the GRU-based model: uniform resource locator (URL) grouping to handle the variant URLs of a Web page and Web embedding to represent both events in a unified vector space. The experimental results based on the real user interaction data showed that click events within an application improved the overall prediction performance by 13.7% on average, which were overlooked by most of the previous research. In addition, WebProfiler achieved an average F-measure of 0.798 for top three candidates where URL grouping and Web embedding contributed to 52.4% of the performance improvement.