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

Widdeson, Kai; Hadžidedić, Sunčica

doi:10.1109/aiccsa56895.2022.10017865

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

Widdeson, Kai; Hadžidedić, Sunčica

Authors

Kai Widdeson

Dr Suncica Hadzidedic suncica.hadzidedic@durham.ac.uk
Assistant Professor

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. (2023). FFM-SVD: A Novel Approach for Personality-aware Recommender Systems. . https://doi.org/10.1109/aiccsa56895.2022.10017865

Conference Name	2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA)
Conference Location	Abu Dhabi, UAE
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

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