Representative-based diversity retrieval

Recently, case-based reasoning has been widely used in electronic commerce by providing product recommendations that are most similar to user's needs. However in some cases, the most similar recommendations are not necessary the ones most acceptable or useful. There is a growing awareness of the need for providing alternatives to similarity-based retrieval, alternatives that attempt to improve the recommendation diversity. In this paper, we present a new retrieval algorithm which is able to optimize the trade-off between similarity and diversity. Experimental results show that the proposed algorithm can achieve a better overall quality than other approaches.