Examining the Relationship Between User Similarity and Diversity/Novelty in Recommendation Models

Abstract

Recommendation systems play an important role in a variety of fields by suggesting items based on user preferences. In recent years, multi-objective recommendations that balance accuracy with diversity and novelty have attracted attention, but they are particularly difficult to apply with cold-start users. This study proposes a foundational approach leveraging the preferences of similar existing users to provide multi-objective recommendations to cold-start users. We evaluated popular recommendation models SVD, LightGCN, and NCF, by examining their capability to capture diversity and novelty. Results indicate that SVD and LightGCN generally capture diversity and novelty effectively. In contrast, NCF consistently struggles to capture diversity and novelty at the embedding layer. These findings support our hypothesis that users with similar embeddings in the latent space also have similar diversity and novelty values, offering a pathway to improve cold-start recommendations through multi-objective frameworks.