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The collaborative filtering algorithm is mainly used to predict users' preferences and realize intelligent recommendation through data mining of users' behaviors. In the practical applications, with the increasing complexity of the contents and users, there are many problems, among which cold-start is the most prominent. In this paper, we proposed a recommendation model (BROCF) of collaborative filtering algorithm based on binary relative comparison sorting, which aims to improve the efficiency of the algorithm and the accuracy of the recommended results. The comparative experiment results showed that the improved algorithm in this paper had significant increase in forecast accuracy, the deviation of improved algorithm between the predictive results and the real value was the lowest comparing with other algorithms, which certified the feasibility and effectiveness of the algorithm.
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