Web Content Recommendation using Machine Learning on User Mouse Tracking Data
Abstract
The websites are becoming more and more dynamic but not intelligent. Based on certain mouse clicks or user choices, todays dynamic websites can mold themselves but cannot predict relevant data intelligently. The data contained in todays websites is growing and the number of users demanding unique different information is also ever increasing. This has created a challenging problem of delivering the right content to every user. This thesis is an original work concentrating on solving this problem of generating relevant content for each individual user. One of the primary inputs used by the project is the mouse movement behavior of the user. If the website capturing mouse movements is built in such a way that the mouse pointer is mostly close to the point of gaze of the user, then the mouse movement behavior would theoretically mean tracking the eye of the user. Based on this mouse movement data, further content can be predicted and personalized for each user using one or more machine learning models. This thesis proposes a complete methodology of building and implementing such a system. As a proof of concept, an online shopping website has been built and further tests have been conducted which gave a remarkable accuracy of 84.09% when compared with the actual needs of the user. The working demonstration of the project along with its description is available online at http://sparshgupta.name/MSc/Project
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