Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorization

Abstract

The conventional method of computing personality scores through extensive questionnaire-based surveys poses practical challenges in real-world scenarios. An alternate route is to predict personality scores from user reviews by analysing various linguistic features such as writing style, word choices, and specific phrases. However, the reviews are domain-dependent and classification models trained on one domain cannot be readily applied to other domains. To mitigate this challenge, we propose a cross-domain recommendation framework called PEMF-CD which leverages a novel mixing strategy to integrate user reviews from multiple domains with common joint embedding space and predict user personality scores using a transformer model. By capturing the underlying semantics and latent representations within the textual data, the transformer architecture can effectively model the linguistic cues to infer users’ personality traits, and the learning is transferred across domains. To further enhance the recommendation process, our model integrates personality-wise and rating pattern-based similarities of users into a probabilistic matrix factorization method that fosters user neighbourhoods based on similarity scores among users. Comprehensive experiments were conducted using five real-world datasets from TripAdvisor and Amazon with varied numbers of users, items, and reviews of up to 44,187, 26,386, and 426,791, respectively. The performance has been benchmarked against thirteen baseline algorithms and the experimental results demonstrate a significant improvements of up to 24.72%, 64.28%, 48.79%, and 61% in RMSE, and 55.9%, 76.7%, 67.6%, and 71.5% in MAE for a 90:10 train–test split with Digital Music, Fashion, Magazine Subscriptions and Video Games datasets from Amazon, respectively. Similar results have been observed for the 80:20 train–test split.