Evaluating the Performance of Navigation Prediction Model Based on Varied Session Length

Abstract

Web navigation prediction plays a vital role in web, due to its broad research applications. It can be used for personalization, improvise website design, and business intelligence. Main aim of these applications is to enhance user’s satisfaction levels who are visiting the website. Web navigation prediction model tries to predict the future set of the webpage from their historical navigations. The past navigations are collected in the web server log file. Navigations form the sessions of varied length which are used for building the navigation model. Selecting very long sessions or very small sessions degrades the model performance. Thus, selecting an optimal session length is mandate as it would impact the model performance positively. This paper presents pre-investigation measures like page loss, branching factor and session length. We investigate the performance of prediction model based on two different ranges of session length. First range that has been considered is three to seven (3 to 7) and second range is two to ten (2 to 10). The Model has been evaluated on three real datasets. The experimental results show that selecting session of length ranging from 2 to 10 gives better learning hence intensifies accuracy of navigation prediction model. The model accuracy of Set B showed improvement from 0.27 to 8.73% in MSWEB, 0.62 to 2.8% in BMS and 10.81 to 14.23% in Wikispeedia dataset.