Please use this identifier to cite or link to this item:
Title: ‘Who likes what and, why?’ Insights into modeling users’ personality based on image ‘likes’
Authors: Guntuku, Sharath Chandra
Zhou, Joey Tianyi
Roy, Sujoy
Lin, Weisi
Tsang, Ivor W.
Keywords: Engineering::Computer science and engineering
Issue Date: 2016
Source: Guntuku, S. C., Zhou, J. T., Roy, S., Lin, W., & Tsang, I. W. (2018). ‘Who likes what and, why?’ Insights into modeling users’ personality based on image ‘likes’. IEEE Transactions on Affective Computing, 9(1), 130-143. doi:10.1109/TAFFC.2016.2581168
Journal: IEEE Transactions on Affective Computing
Abstract: 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.
ISSN: 1949-3045
DOI: 10.1109/TAFFC.2016.2581168
Rights: © 2016 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

Citations 20

Updated on Mar 10, 2021

Citations 20

Updated on Mar 4, 2021

Page view(s)

Updated on Jan 24, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.