[Objective] This study aims to solve the issues facing traditional methods measuring item similarity, such as using common rating and poor prediction accuracy in highly sparse data environment. [Methods] First, we constructed the dissimilarity coefficient with the increment of diversity from bioinformatics. Then, we calculated item similarity according to the frequency and distribution of ratings, which effectively addressed the data sparsity issue. Finally, we improved the accuracy of measurement with the item attributes. [Results] Compared with traditional algorithms, the proposed method reduced RMSE by 2.56%, and then increased the F value by 3.88%. [Limitations] The diversity of our recommendation might be insufficient. [Conclusions] The proposed method could effectively measure item similarity.
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