A Diversity-aware Approach to Bundle Recommendations

Abstract

Recommendation systems help users navigate vast amounts of data, with bundle recommendation systems enhancing personalization and customized experience by grouping related items. However, many existing methods overemphasize relevance, leading to repetitive suggestions and user fatigue. This paper introduces two novel bundling methods—Bundle Partition and Bundle Function—designed to balance both diversity and relevance. These methods were evaluated using Amazon datasets on the Appliances, All_Beauty, and Luxury_Beauty categories. Results show a significant increase in diversity, as measured by Intra-List Diversity (ILD), while maintaining high relevance through average ratings. Furthermore, the novelty, assessed via Mean Inverse User Frequency (MIUF), indicates that these methods offer a fresh and relevant experience. These findings emphasize the importance of diversity in enhancing user engagement.