FFM-SVD: A Novel Approach for Personality-aware Recommender Systems

Widdeson, Kai; Hadžidedić, Sunčica

Abstract

This paper addresses and evaluates approaches to incorporating personality data into a recommender system. Automatic personality recognition is enabled by the LIWC dictionary. Personality-aware pre-filtering techniques are developed and discussed, with the introduced non-targeted stratified personality sampling performing the best. A novel personality-aware model, FFM-SVD, is proposed and shown to outperform alternative models in prediction accuracy.

Citation

Widdeson, K., & Hadžidedić, S. (2022, December). FFM-SVD: A Novel Approach for Personality-aware Recommender Systems. Presented at 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA), Abu Dhabi, UAE

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA)
• Start Date: Dec 5, 2022
• End Date: Dec 7, 2022
• Acceptance Date: Sep 27, 2022
• Online Publication Date: Jan 20, 2023
• Publication Date: 2023-01
• Deposit Date: Oct 25, 2022
• Publicly Available Date: Jul 28, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-8
• DOI: https://doi.org/10.1109/aiccsa56895.2022.10017865
• Public URL: https://durham-repository.worktribe.com/output/1135810
• Additional Information: 5-8 Dec. 2022