‘Who Likes What and, Why?’ Insights into Modeling Users’ Personality Based on Image ‘Likes

• Published in: IEEE transactions on affective computing 2018-01, Vol.9 (1), p.130-143
• Main Authors: Guntuku, Sharath Chandra, Zhou, Joey Tianyi, Roy, Sujoy, Weisi Lin, Tsang, Ivor W.
• Format: Article
• Language: English
• Subjects: Affective computing; Big five factor; Computational modeling; Data models; Digital media; Feature extraction; image recommendation; Machine learning; Media; Modelling; Multimedia; ordinal regression; Personality; personality modeling; Personality traits; Predictive models; semantic features; Semantics; State of the art
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2016.2581168

The increased proliferation of data production technologies (e.g., cameras) and consumption avenues (e.g., social media) has led to images and videos being utilized by users to convey innate preferences and tastes. This has opened up the possibility of using multimedia as a source for user-modeling. This work attempts to model personality traits (based on the Five Factor Theory) of users using a collection of images they tag as `favorite' (or like) on Flickr. First, a set of semantic features are proposed to be used for representing different concepts in images which influence users to like them. The addition of the proposed features led to improvement over state-of-the-art by 12 percent. Second, a novel machine learning approach is developed to model users' personality based on the image features (resulting in upto 15 percent improvement). Third, efficacy of the semantic features and the modeling approach is shown in recommending images based on personality modeling. Using the modeling approach, recommendations are made regarding the factors that might influence users with different personality traits to like an image.