The paper introduces a novel pre-processing approach to enhance the diversity of personalized recommender systems. The key highlights are:
The proposed approach employs a user-centric pre-processing strategy that selectively adds and removes a percentage of interactions from user profiles. This personalization ensures the recommender system remains closely aligned with user preferences while gradually introducing distribution shifts.
The pre-processing technique offers flexibility and can seamlessly integrate into any recommender architecture. Extensive experiments are conducted on two public datasets for news and book recommendations, testing various standard and neural network-based recommender algorithms.
The results show that the pre-processed data leads to recommender systems achieving comparable or improved performance compared to those trained on original data. Regarding diversity, the approach consistently improves normative diversity metrics like calibration, while descriptive diversity measures like coverage show mixed results.
Additionally, the pre-processed data results in higher fair-nDCG scores, indicating enhanced exposure fairness and better representation of minority categories.
The authors conclude that their user-centric pre-processing approach can effectively diversify recommendations without compromising accuracy, promoting both user satisfaction and provider fairness.
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by Manel Slokom... at arxiv.org 05-06-2024
https://arxiv.org/pdf/2405.02156.pdfDeeper Inquiries