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Eye-tracking-based personality prediction with recommendation interfaces

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Abstract

Recent research in behavioral decision making demonstrates the advantages of using eye-tracking to surface insights into users’ underlying cognitive processes. Personality, according to psychology definition, accounts for individual differences in our enduring emotional, interpersonal, experiential, attitudinal, and motivational styles. In recommender systems (RS), it has been found that user personality is related to their preferences and behavior, which attracted an increasing attention to the ways to leverage personality into the recommendation process. However, accurate acquisition of a user’s personality is still a challenging issue. In this work, we investigate the possibility of automatically detecting personality from users’ eye movements when interacting with a recommendation interface. Specifically, we report an experiment that harnesses two recommendation interfaces to collect eye-movement data in several product domains and then utilize the data to predict the users’ Big-Five personality traits through various machine learning methods. The results show that AdaBoost combined with Gini index score-based feature selector predicts the traits most accurately, and interface- and domain-specific data allow to improve the accuracy of personality trait predictions. Our findings could inform personality-based RS by improving the process of indirect user personality acquisition.

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Notes

  1. Technical specifications at https://www.tobiipro.com/product-listing/tobii-pro-x3-120/. The gaze precisions are 0.34 and 0.24 under monocular and binocular conditions, respectively.

  2. http://mobile.zol.com.cn/.

  3. Explicit user ratings for an attribute were averaged as the sentiment score. If ratings were unavailable, feature-level opinion mining of the product reviews was applied to infer the sentiment score (Chen and Wang 2017).

  4. http://movie.mtime.com/.

  5. http://www.booking.com/.

  6. We adopted a validated Chinese version of BFI in our experiment (Carciofo 2016).

  7. The experiment was originally conducted in a within-subjects design where each participant was asked to interact with both types of interfaces in a random order. For this work, we only considered the first interface they used, in which case the experimental procedure was simplified into three steps.

  8. After the calibration procedure, the participants were asked to stay approximately 60–65 cm away from the eye tracker when performing the task, as per the eye-tracker’s manual.

  9. The experimental procedure was approved by the University Research Ethics Committee.

  10. The Tobii I-VT fixation filter was used. During the filtering process, if there were no gaze data within two consecutive seconds in a recording, this recording was removed.

  11. https://scikit-learn.org.

  12. https://github.com/jundongl/scikit-feature.

  13. https://github.com/wanlingcai1997/personality_prediction_code.git.

  14. Accuracy refers to the proportion of correct predictions (i.e., low or high class label being predicted) among all the predictions.

  15. In another experiment, we varied the number of features from 10 to 80 with a step of 10. The results showed that the highest accuracy was achieved when the number of features is below 40.

  16. Impurity measures how often a random element is incorrectly labeled according to the class distribution in the data.

  17. This test was chosen owing to its ability to determine whether three or more group means (i.e., the nine classifiers in our case) are significantly different, where the participants are the same in each group (Howell 2012). We further conducted post hoc dependent sample t test for pairwise comparisons. All the reported significance tests were performed on the tenfold cross-validation results.

  18. As there were a total of 36 pairwise comparisons among the 9 classifiers, the Bonferroni-corrected p value was calculated by multiplying the uncorrected p value by 36.

  19. We chose this test to compare the means of two independent groups (two recommendation interfaces in our case), as the participants are different between the two groups.

  20. The one-way ANOVA test was used for comparing more than two independent groups (i.e., three product domains in our case) (Howell 2012).

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Acknowledgements

This work was supported by Hong Kong Research Grants Council (project RGC/HKB U12201620) and partially by Hong Kong Baptist University (IRCMS Project IRCMS/19-20/D05). We also thank all participants for their time in taking part in our experiment and reviewers for their valuable comments on our manuscript.

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  1. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

    Li Chen & Wanling Cai

  2. School of Mechanical Engineering, Shandong University, Jinan, Shandong, China

    Dongning Yan

  3. Centre for Health Informatics, Macquarie University, Sydney, Australia

    Shlomo Berkovsky

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  1. Li Chen
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Interface screenshots

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Fig. 7
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LIST interface (left) and ORG interface (right) for movies

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Fig. 8
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LIST interface (left) and ORG interface (right) for hotels

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Chen, L., Cai, W., Yan, D. et al. Eye-tracking-based personality prediction with recommendation interfaces. User Model User-Adap Inter 33, 121–157 (2023). https://doi.org/10.1007/s11257-022-09336-9

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  • DOI: https://doi.org/10.1007/s11257-022-09336-9

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