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Evaluating Automatic Personality Recognition (APR) Approaches: The Role of Deep Learning in Enhancing Recommender System Performance [software] Open Access

This shared folder contains an end-to-end experimental pipeline for developing and evaluating personality-aware models and recommender systems using text data. It integrates two datasets—PANDORA (non-public) and Amazon Reviews 2023 (Movies & TV), supplemented by Reddit data for additional preprocessing and linguistic analysis. The workflow begins with data cleaning and preparation, followed by LIWC-based psychological feature extraction and transformer-ready formatting. The modelling component includes a BERT-based personality classification framework (APR model), supporting multiple label configurations (10-class and binary) and optional regression. Trained models are applied to Amazon data for inference.

Descriptions

Resource type

Software

Contributors

Creator: Law, Effie Lai-Chong ¹
Contact person: Law, Effie Lai-Chong ¹
Data curator: Deacon, Eilidh ¹

¹ Durham University, UK

Funder

Research methods

Other description

Keyword

Personality Computing
Human-computer Interaction

Subject

Personality assessment--Computer programs
Human-computer interaction

Location

Language

Cited in

Identifier

ark:/32150/r2sx61dm35r
doi:10.15128/r2sx61dm35r

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

File Details

Depositor

E.L. Law

Date Uploaded

19 June 2026, 13:06:11

Date Modified

24 June 2026, 16:06:38

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Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 198041

Last modified: 2026:06:19 14:54:38+01:00

Filename: mtrt72_L4_Project_Code.zip

Original checksum: e1755a3cb32f2134ac5af30e307f71d4