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Predicting Users’ Movie Preference and Rating Behavior from Personality and Values

Predicting Users’ Movie Preference and Rating Behavior from Personality and Values In this article, we propose novel techniques to predict a user’s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one’s decision or choice in real life. In this work, we integrate user interactions in Twitter and IMDb to derive interesting relations between human psychological attributes and their movie preferences. In particular, we first predict a user’s movie genre preferences from the personality and value scores of the user derived from her tweets. Second, we also develop models to predict user movie rating behavior from her tweets in Twitter and movie genre and storyline preferences from IMDb. We further strengthen the movie rating model by incorporating the user reviews. In the above models, we investigate the role of personality and values independently and combinedly while predicting movie genre preferences and movie rating behaviors. We find that our combined models significantly improve the accuracy than that of a single model that is built by using personality or values independently. We also compare our technique with the traditional movie genre and rating prediction techniques. The experimental results show that our models are effective in recommending movies to users. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Predicting Users’ Movie Preference and Rating Behavior from Personality and Values

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/3338244
Publisher site
See Article on Publisher Site

Abstract

In this article, we propose novel techniques to predict a user’s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one’s decision or choice in real life. In this work, we integrate user interactions in Twitter and IMDb to derive interesting relations between human psychological attributes and their movie preferences. In particular, we first predict a user’s movie genre preferences from the personality and value scores of the user derived from her tweets. Second, we also develop models to predict user movie rating behavior from her tweets in Twitter and movie genre and storyline preferences from IMDb. We further strengthen the movie rating model by incorporating the user reviews. In the above models, we investigate the role of personality and values independently and combinedly while predicting movie genre preferences and movie rating behaviors. We find that our combined models significantly improve the accuracy than that of a single model that is built by using personality or values independently. We also compare our technique with the traditional movie genre and rating prediction techniques. The experimental results show that our models are effective in recommending movies to users.

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Oct 15, 2020

Keywords: Psychological attributes: personality and values

References