This paper, we focus on recommendation functions to extract the high potential sales items from the trend leaders’ activities with the ID (Identification)-POS (Point-Of-Sales) data. Although the recommendation system is in common among the B2B or B2C businesses, the conventional recommendation engines provide the proper results; therefore, we need to improve the algorithms for the recommendation. We have defined the index of the trend leader with the criteria for the day and the sales number. Using with the results, we are able to make detailed decisions in the following three points: 1) to make appropriate recommendations to the other group member based on the transitions of the trend leaders’ preferences; 2) to evaluate the effect of the recommendation with the trend leaders’ preferences; and 3) to improve the retail management processes: prevention from the stock-out, sales promotion for early purchase effects and the increase of the numbers of sales.
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