Automatic Recommendation for Online Users Using Web Usage Mining

TL;DR

This paper presents a web usage mining-based recommendation system that predicts user navigation patterns and suggests relevant pages, improving the accuracy of capturing user intuition in online environments.

Contribution

It introduces a two-tier architecture leveraging web usage mining to enhance personalized recommendations based on user behavior and similarity profiles.

Findings

Improved accuracy in user intuition capturing.

Effective prediction of navigation patterns.

Enhanced recommendation relevance.

Abstract

A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user's navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user's having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.