As Internet expanding into offline, the traditional retail industry began to use the personalized recommendation algorithm to increase user stickiness, conversion and business income. Without considering the data segmentation problem, traditional recommendation algorithm did not perform well in the traditional business data. Accordingly, we considered the interest spread characteristic of retail industry behavior, adopted the method of complex network to construct a personalized recommendation algorithm using the segmentation data set. By using a real sales dataset of a large supermarket, we provided an evaluation of our algorithm. The results show that our algorithm have much better performance in accuracy and recall than the traditional ones, but with the disadvantage of being less coverage.
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