In this paper, we introduce a novel approach to improve the diversity of Top-N recommendations while maintaining recommendation performance. 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. To evaluate our approach, we run extensive experiments on two publicly available data sets for news and book recommendations. 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, leveraging 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 or niche categories.

翻译：本文提出了一种新颖的方法，用于在保持推荐性能的同时，提升Top-N推荐的多样性。该方法采用以用户为中心的预处理策略，旨在让用户接触到更广泛的内容类别和主题。我们通过选择性地从用户画像中增加或移除一定比例的用户交互来个性化这一策略，从而确保在逐步引入分布变化的同时，仍能紧密贴合用户的偏好。该预处理技术具有灵活性，可无缝集成到任何推荐系统架构中。为评估效果，我们在两个公开数据集上（分别用于新闻和书籍推荐）进行了大量实验，测试了多种标准及基于神经网络的推荐系统算法。结果表明，我们的方法能够生成多样化的推荐，确保用户接触到更广泛的物品。此外，使用预处理后的数据进行训练，可使推荐系统达到与原始未修改数据训练相当，甚至更优的性能水平。同时，该方法通过促进少数或小众类别的曝光，提升了推荐系统的提供方公平性。