The importance of being dissimilar in Recommendation
This is an incremental improvement for recommendation systems, potentially enhancing user experience.
The paper tackled the problem of improving recommendation accuracy by incorporating a formal notion of dissimilarity alongside similarity in nearest-neighbor approaches, showing effectiveness in terms of accuracy results.
Similarity measures play a fundamental role in memory-based nearest neighbors approaches. They recommend items to a user based on the similarity of either items or users in a neighborhood. In this paper we argue that, although it keeps a leading importance in computing recommendations, similarity between users or items should be paired with a value of dissimilarity (computed not just as the complement of the similarity one). We formally modeled and injected this notion in some of the most used similarity measures and evaluated our approach showing its effectiveness in terms of accuracy results.