WBPL: An open-source library for predicting web surfing behaviors

Abstract

We present WBPL (Web users Behavior Prediction Library), a cross-platform open-source library for predicting the behavior of web users. WBPL allows training prediction models from server logs. The proposed library offers support for three of the most used webservers (Apache, Nginx and Lighttpd). Models can then be used to predict the next resources fetched by users and can be updated with new logs efficiently. WBPL offers multiple state-of-the-art prediction models such as PPM, All-K-Order-Markov and DG and a novel prediction model CPT (Compact Prediction Tree). Experiments on various web click-stream datasets shows that the library can be used to predict web surfing or buying behaviors with a very high overall accuracy (up to 38 %) and is very efficient (up to 1000 predictions /s). © 2014 Springer International Publishing.