The Analysis And Research On The Behavior Of Shopping Users

With the development of science and technology in our country and the accelerated pace of life,online shopping with its time,geographical and product selection,and other aspects of the advantages are favored by Internet users.The expansion of the scale of China’s online shopping market and the surge in the number of online shoppers prompt e-commerce platform to generate a large number of e-commerce browsing and sales data.Facing the numerous and complicated browsing and sales data,businesses need to take in-depth analysis and understanding of data to and digging out the needs of potential users,and to develop a variety of sales strategy to meet the user’s needs,and thus win more customers for e-commerce platform.To this end,this paper will start from the browsing and buying behavior of electronic commerce users,using statistical analysis and data mining methods to analyze and mine user’s behavior data.Existing research shows that there is a strong relationship between users’ browsing track and their purchasing behavior.Therefore,through analyze and research the collected data of the user’s behavior,it can be better to dig out the users tend to buy goods,that is to say which users are interested,and so it can be a good understanding of the needs of users.The main work of this paper is as follows:(1)This paper regards the user purchasing behavior as the accumulation of user browsing.This paper carries on statistical analysis of a large number of the behavior trajectory data.And then according to the above point of view,using a commercial pheromone mining algorithm based on the idea from pheromone the ant colony algorithm.Here commodity information element value is on behalf of degree of interest of user in goods.(2)This paper is on the basis of previous studies to improve the solution of pheromone of product,because the previous algorithm in solving the commodity information hormone without taking into account the goods corresponding to the browsing,collection,add to cart,the purchase of different user behavior,user behavior of different represent the user of commodities in different,so in demand product pheromone cannot be regarded as the same,and should be treated differently.Since the interests will decay gradually over time,it need s to add attenuation factor to solve the problem,making some unpopular commodities not always have a very high pheromones of product,to form a good transition from the old situation.(3)The last part is the experimental part.Through experimental comparison,the improved method in the analysis of user’s behavior data has great advantage on the experimental results.The results show that the improved method is better than before.At the same time,the data can be maximized.