  In search and recommendation, diversifying the multi-aspect search results
could help with reducing redundancy, and promoting results that might not be
shown otherwise. Many previous methods have been proposed for this task.
However, previous methods do not explicitly consider the uniformity of the
number of the items' classes, or evenness, which could degrade the search and
recommendation quality. To address this problem, we introduce a novel method by
adapting the Simpson's Diversity Index from biology, which enables a more
effective and efficient quadratic search result diversification algorithm. We
also extend the method to balance the diversity between multiple aspects
through weighted factors and further improve computational complexity by
developing a fast approximation algorithm. We demonstrate the feasibility of
the proposed method using the openly available Kaggle shoes competition
dataset. Our experimental results show that our approach outperforms previous
state of the art diversification methods, while reducing computational
complexity.
