Analyzing Performance of Deep Learning Techniques for Web Navigation Prediction (original) (raw)

Procedia Computer Science, 2020

Abstract

Abstract The weblog is dynamic and its size is growing exponentially with time in terms of navigation sessions. These stored sessions are used for Web Navigation Prediction (WNP). Each user had varied behavior on the web so is their navigated sessions. With a variety of large dynamic sessions, the task of navigation prediction is becoming challenging. There is a need for an effective method to handle large sessions with multiple labels for predicting user desired information. This paper analyses the performance of Deep Learning techniques like Multi-Layer Perceptron and Long-Short Term Memory based on parameters like number of hidden units, number of layers, activation function, optimization function, learning rate, and batch size. The networks were trained on six experimental parameter setups to form 216 models. The performances of these models are evaluated on two real datasets: BMS and CTI. It has been observed that Long-Short Term Memory performs best on most of the setups.

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