Analyzing the Impact of Domain Similarity: A New Perspective in Cross-Domain Recommendation
Ajay Krishna Vajjala, Arun Krishna Vajjala, Ziwei Zhu, David S. Rosenblum · 2024
Cross-domain recommendation (CDR) has recently emerged as an effective way to alleviate the cold-start and sparsity issues faced by recommender systems, by transferring information from an auxiliary domain to a target domain to improve recommendations. Studying the similarity between domains is a novel direction in CDR research, potentially opening doors for further exploration. In this context, we introduce a systematic approach to quantify similarity between a pair of domains and explore how current CDR methods perform with both similar and dissimilar domain combinations. We achieve this by presenting two original similarity metrics. Our extensive empirical evaluation on different domain combinations demonstrates that the state-of-the-art CDR algorithms do not perform significantly better when using source domains that are more similar to the target domain, compared to those that are less similar. Importantly, we find that no matter how similarity is measured, it does not correlate with the recommendation performance of the state-of-the-art algorithms.