Weighted Slope One algorithm combining Bhattacharyya coefficient and comprehensive similarity
To solve the problem that the traditional weighted Slope One algorithm relies too much on users'common scoring items,which lead to low prediction accuracy in sparse datasets,an improved weighted Slope One algorithm combining Bab-bitt coefficient and comprehensive similarity is proposed.Firstly,the calculation method of user similarity is improved by in-troducing Bhattacharyya coefficient and user behavior preference,according to which the nearest neighbor set to be predicted is selected;Secondly,in order to optimize the prediction score,the project similarity is calculated by using Bhattacharyya co-efficient and project popularity,and it is incorporated into the score calculation as a weight factor.Finally,the proposed algo-rithm is compared with several representative algorithms under different nearest neighbor numbers.The simulation results show that the proposed algorithm can effectively overcome the defect of low prediction accuracy in the case of too sparse datasets and improve the recommendation accuracy.
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