How to Diversify any Personalized Recommender?

TL;DR

This paper presents a user-centric pre-processing method to enhance diversity in Top-N recommendations across various recommender systems, maintaining accuracy and promoting fairness.

Contribution

It introduces a flexible, personalization-based pre-processing technique that can be integrated into any recommender architecture to improve diversity and fairness.

Findings

Increases recommendation diversity without sacrificing accuracy.

Achieves comparable or better performance than original data training.

Enhances exposure to minority categories, promoting fairness.

Abstract

In this paper, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining accuracy. Our approach employs a user-centric pre-processing strategy aimed at exposing users to a wide array of content categories and topics. We personalize this strategy by selectively adding and removing a percentage of interactions from user profiles. This personalization ensures we remain closely aligned with user preferences while gradually introducing distribution shifts. Our pre-processing technique offers flexibility and can seamlessly integrate into any recommender architecture. We run extensive experiments on two publicly available data sets for news and book recommendations to evaluate our approach. We test various standard and neural network-based recommender system algorithms. Our results show that our approach generates diverse recommendations, ensuring users are exposed to a wider range of items. Furthermore, using pre-processed data for training leads to recommender systems achieving performance levels comparable to, and in some cases, better than those trained on original, unmodified data. Additionally, our approach promotes provider fairness by facilitating exposure to minority categories. Our GitHub code is available at: https://github.com/SlokomManel/How-to-Diversify-any-Personalized-Recommender-