Contextual Personality-Aware Recommender System Versus Big Data Recommender System

Authors

DOI:

https://doi.org/10.52825/bis.v1i.38

Keywords:

Recommender system, Predictive modelling, Big five, Personality traits, Big data analytics,, Amazon dataset

Abstract

Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.

Downloads

Download data is not yet available.

References

P. B. Thorat, R. Goudar, and S. Barve, “Survey on collaborative filtering, content-based filtering and hybrid recommendation system,” International Journal of Computer Applications, vol. 110, no. 4, pp. 31–36, 2015.

Y. Koren and R. Bell, “Advances in collaborative filtering,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston, MA: Springer US, 2011, pp. 145–186, ISBN: 978-0-387-85820-3.

G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE transactions on knowledge and data engineering, vol. 17, no. 6, pp. 734–749, 2005.

M. Onori, A. Micarelli, and G. Sansonetti, “A comparative analysis of personality-based music recommender systems.,” in Empire@ RecSys, 2016, pp. 55–59.

P. Potash and A. Rumshisky, “Recommender system incorporating user personality profile through analysis of written reviews.,” in EMPIRE@ RecSys, 2016, pp. 60–66.

F. A. Paiva, J. A. Costa, and C. R. Silva, “A personality-based recommender system for semantic searches in vehicles sales portals,” in International Conference on Hybrid Artificial Intelligence Systems, Springer, 2017, pp. 600–612.

M. R. Barrick and M. K. Mount, “The big five personality dimensions and job performance: A meta-analysis,” Personnel psychology, vol. 44, no. 1, pp. 1–26, 1991.

S. V. Paunonen and M. C. Ashton, “Big five factors and facets and the prediction of behavior.,” Journal of personality and social psychology, vol. 81, no. 3, p. 524, 2001.

D. H. Kluemper, P. A. Rosen, and K. W. Mossholder, “Social networking websites, personality ratings, and the organizational context: More than meets the eye? 1,” Journal of Applied Social Psychology, vol. 42, no. 5, pp. 1143–1172, 2012.

D. Azucar, D. Marengo, and M. Settanni, “Predicting the big 5 personality traits from digital footprints on social media: A meta-analysis,” Personality and individual differences, vol. 124, pp. 150–159, 2018.

C. S. Hall, G. Lindzey, and J. B. Campbell, “Theories of personality,” Wiley New York,Tech. Rep., 1957.

M. Zuckerman, D. M. Kuhlman, J. Joireman, P. Teta, and M. Kraft, “A comparison of three structural models for personality: The big three, the big five, and the alternative five.,” Journal of personality and social psychology, vol. 65, no. 4, p. 757, 1993.

D. Cervone and L. A. Pervin, Personality: Theory and research. John Wiley & Sons, 2015.

J. M. Digman, “Personality structure: Emergence of the five-factor model,” Annual review of psychology, vol. 41, no. 1, pp. 417–440, 1990.

L. R. Goldberg, “The structure of phenotypic personality traits.,” American psychologist, vol. 48, no. 1, p. 26, 1993.

O. P. John, S. Srivastava, et al., “The big five trait taxonomy: History, measurement, and theoretical perspectives,” Handbook of personality: Theory and research, vol. 2, no. 1999, pp. 102–138, 1999.

B. d. E. Raad and M. E. Perugini, Big five factor assessment: Introduction. Hogrefe & Huber Publishers, 2002.

G. V. Caprara, C. Barbaranelli, L. Borgogni, and M. Perugini, “The “big five questionnaire”: A new questionnaire to assess the five factor model,” Personality and individual Differences, vol. 15, no. 3, pp. 281–288, 1993.

C. Barbaranelli, G. V. Caprara, A. Rabasca, and C. Pastorelli, “A questionnaire for measuring the big five in late childhood,” Personality and individual differences, vol. 34, no. 4, pp. 645–664, 2003.

D. Blackwell, C. Leaman, R. Tramposch, C. Osborne, and M. Liss, “Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction,” Personality and Individual Differences, vol. 116, pp. 69–72, 2017.

D. J. Kuss and M. D. Griffiths, “Online social networking and addiction—a review of the psychological literature,” International journal of environmental research and public health, vol. 8, no. 9, pp. 3528–3552, 2011.

D. Azucar, D. Marengo, and M. Settanni, “Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis,” Personality and Individual Differences, vol. 124, no. September 2017, pp. 150–159, 2018, ISSN: 01918869. DOI: 10.1016/j.paid.2017.12.018. [Online]. Available: https://doi.org/10.1016/j.paid.2017.12.018.

B. Y. Pratama and R. Sarno, “Personality classification based on twitter text using naive bayes, knn and svm,” in 2015 International Conference on Data and Software Engineering (ICoDSE), IEEE, 2015, pp. 170–174.

N. Majumder, S. Poria, A. Gelbukh, and E. Cambria, “Deep learning-based document modeling for personality detection from text,” IEEE Intelligent Systems, vol. 32, no. 2, pp. 74–79, 2017.

Y. Mehta, N. Majumder, A. Gelbukh, and E. Cambria, “Recent trends in deep learning based personality detection,” Artificial Intelligence Review, pp. 1–27, 2019.

H. Ahmad, M. Z. Asghar, A. S. Khan, and A. Habib, “A systematic literature review of personality trait classification from textual content,” Open Computer Science, vol. 10, no. 1, pp. 175–193, 2020.

A. Roshchina, J. Cardiff, and P. Rosso, “User profile construction in the twin personality based recommender system,” in Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011), 2011, pp. 73–79.

——, “Evaluating the similarity estimator component of the twin personality-based recommender system.,” in LREC, 2012, pp. 4098–4102.

A. Roshchina, “TWIN : Personality-based Recommender System,” 2012.

A. Roshchina, J. Cardiff, and P. Rosso, “A comparative evaluation of personality estimation algorithms for the twin recommender system,” in Proceedings of the 3rd international workshop on Search and mining user-generated contents, 2011, pp. 11–18.

M. Elahi, M. Braunhofer, F. Ricci, and M. Tkalcic, “Personality-based active learning for collaborative filtering recommender systems,” in Congress of the Italian Association for Artificial Intelligence, Springer, 2013, pp. 360–371.

M. Tkalcic and L. Chen, “Personality and recommender systems,” in Recommender systems handbook, Springer, 2015, pp. 715–739.

T. T. Nguyen, F. M. Harper, L. Terveen, and J. A. Konstan, “User personality and user satisfaction with recommender systems,” Information Systems Frontiers, vol. 20, no. 6, pp. 1173–1189, 2018.

Y. Huang, H. Liu, W. Li, Z. Wang, X. Hu, and W. Wang, “Lifestyles in amazon: Evidence from online reviews enhanced recommender system,” International Journal of Market Research, vol. 62, no. 6, pp. 689–706, 2020.

M. Szmydt, “How do movie preferences correlate with e-commerce purchases? an empirical study on amazon,” in International Conference on Business Information Systems, Springer, 2020, pp. 184–196.

S. Yakhchi, A. Beheshti, S. M. Ghafari, and M. Orgun, “Enabling the analysis of personality aspects in recommender systems,” arXiv preprint arXiv:2001.04825, 2020.

S. Khodabandehlou, S. A. H. Golpayegani, and M. Z. Rahman, “An effective recommender system based on personality traits, demographics and behavior of customers in time context,” Data Technologies and Applications, 2020.

G. Adomavicius and A. Tuzhilin, “Context-aware recommender systems,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston, MA: Springer US, 2011, pp. 217–253, ISBN: 978-0-387-85820-3.

T. Zhou, H. Shan, A. Banerjee, and G. Sapiro, “Kernelized probabilistic matrix factorization: Exploiting graphs and side information,” Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, pp. 403–414, 2012. DOI: 10.1137/1.9781611972825.35.

G. Bouchard, D. Yin, and S. Guo, “Convex collective matrix factorization,” in Artificial intelligence and statistics, PMLR, 2013, pp. 144–152.

S. Gunasekar, M. Yamada, D. Yin, and Y. Chang, “Consistent collective matrix completion under joint low rank structure,” in Artificial Intelligence and Statistics, PMLR, 2015, pp. 306–314.

J. Ni, J. Li, and J. McAuley, “Justifying recommendations using distantly-labeled reviews and fine-grained aspects,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019, pp. 188–197.

A. I. Hariadi and D. Nurjanah, “Hybrid attribute and personality based recommender system for book recommendation,” in 2017 International Conference on Data and Software Engineering (ICoDSE), IEEE, 2017, pp. 1–5.

T. U. Haque, N. N. Saber, and F. M. Shah, “Sentiment analysis on large scale amazon product reviews,” in 2018 IEEE International Conference on Innovative Research and Development (ICIRD), IEEE, 2018, pp. 1–6.

I. Dematis, E. Karapistoli, and A. Vakali, “Fake review detection via exploitation of spam indicators and reviewer behavior characteristics,” in International Conference on Current Trends in Theory and Practice of Informatics, Springer, 2018, pp. 581–595.

J. Wieser, Personality prediction from text, https://github.com/jkwieser/personality-detection-text, 2020.

J. W. Pennebaker and L. A. King, “Linguistic styles: Language use as an individual difference.,” Journal of personality and social psychology, vol. 77, no. 6, p. 1296, 1999.

M. Gjurkovic´ and J. Sˇ najder, “Reddit: A gold mine for personality prediction,” in Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, 2018, pp. 87–97.

R. Katarya and O. P. Verma, “An effective collaborative movie recommender system with cuckoo search,” Egyptian Informatics Journal, vol. 18, no. 2, pp. 105–112, 2017.

T. Mohammadpour, A. M. Bidgoli, R. Enayatifar, and H. H. S. Javadi, “Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm,” Genomics, vol. 111, no. 6, pp. 1902–1912, 2019.

Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol. 42, no. 8, pp. 30–37, 2009.

Published

2021-07-02