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A Stock Recommendation Algorithm base on the Trading Behavior of Stock Investors

Songhe Jin, Tong Sun, Baowei Song


In the field of stock recommendation, current algorithms have the disadvantages of lower precision and unsatisfactory on personalized demand. This paper presents an algorithm based on the trading behavior of stock investors. Firstly stock investors are divided into several groups according to their behaviors. Then stock preference model is constructed for each group, and the standard of stock screening is determined. Finally the similarities between stocks and standard are calculated, and then stocks are recommended for the stock investors according to the similarities. The results of experiments show that the algorithm can effectively improve the precision of stock recommendation.


trading behavior, recommendation system, stock preference model, precision

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