| Summary: | With the development of business intelligence system and data mining technology,user behavior data has an important impact on enterprise decision-making.For the network e-commerce platform,the results of these data analysis can be used to push items of interest to specific users,which can enhance the user experience and the business value of the platform.A personalized recommendation algorithm based on user behavior analysis was proposed,which transforms user behavior information into user rating matrix,and an improved regularized nonnegative matrix decomposition algorithm was also proposed,which adds bias information to the original regularized nonnegative matrix decomposition.This algorithm can fully mine the user behavior information such as click,purchase,browse,collect,etc.,and actively push the items of interest to the users.The experimental results verify the effectiveness and efficiency of the algorithm.
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