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Pure methods generally perform excellently in either recommendation accuracyor diversity, whereas hybrid methods generally outperform pure cases in bothrecommendation accuracy and diversity, but encounter the dilemma of optimalhybridization parameter selection for different recommendation focuses. In thisarticle, based on a user-item bipartite network, we propose a datacharacteristic based algorithm, by relating the hybridization parameter to thedata characteristic. Different from previous hybrid methods, the presentalgorithm adaptively assign the optimal parameter specifically for eachindividual items according to the correlation between the algorithm and theitem degrees. Compared with a highly accurate pure method, and a hybrid methodwhich is outstanding in both the recommendation accuracy and the diversity, ourmethod shows a remarkably promotional effect on the long-standing challengingproblem of the cold start, as well as the recommendation diversity, whilesimultaneously keeps a high overall recommendation accuracy. Even compared withan improved hybrid method which is highly efficient on the cold start problem,the proposed method not only further improves the recommendation accuracy ofthe cold items, but also enhances the recommendation diversity. Our work mightprovide a promising way to better solving the personal recommendation from theperspective of relating algorithms with dataset properties.

Promotional effect on cold start problem and diversity in a data characteristic based recommendation method