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Predicting users’ behavior using mouse movement information: an information foraging theory perspective

  • Amit Kumar Jaiswal
    ,
  • Prayag Tiwari
    ,
  • M. Shamim Hossain
Research Output: Contribution to journal Article Peer-review

Open access

Abstract

The prediction of users’ behavior is essential for keeping useful information on the web. Previous studies have used mouse cursor information in web usability evaluation and designing user-oriented search interfaces. However, we know fairly to a small extent pertaining to user behavior, specifically clicking and navigating behavior, for prolonged search session illustrating sophisticated search norms. In this study, we perform extensive analysis on a mouse movement activities dataset to capture every users’ movement pattern using the effects of information foraging theory (IFT). The mouse cursor movement information dataset includes the timing and positioning information of mouse cursors collected from several users in different sessions. The tasks vary in two dimensions: (1) to determine the interactive elements (i.e., information episodes) of user interaction with the site; (2) adopt these findings to predict users’ behavior by exploiting the LSTM model. Our model is developed to find the main patterns of the user’s movement on the site and simulate the behavior of users’ mouse movement on any website. We validate our approach on a mouse movement dataset with a rich collection of time and position information of mouse pointers in which searchers and websites are annotated by web foragers and information patches, respectively. Our evaluation shows that the proposed IFT-based effects provide an LSTM model a more accurate interpretative exposition of all the patterns in the movement of the users’ mouse cursors across the screen.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 23767-23780

Journal (Volume, Issue Number)

Neural Computing and Applications (Volume 35, Issue 33)

Publication milestones

  • Accepted/In press - 18/08/2020
  • Published - 31/08/2020

Publication status

Published - 31/08/2020

ISSN

0941-0643

External Publication IDs

  • handle.net: 10547/626115
  • Scopus: 85089972553

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