Log filtering method based on user behaviors

Abstract

Abstract In the big data environment, various websites on the Internet have generated more and more user behaviors. Designing a universal log filtering method based on user behaviors is the current research trend. However, the current log filtering technology has disadvantages such as low filtering accuracy and low efficiency. In this paper, we propose a log filtering method based on user behaviors. First, divide user behaviors into multiple sub-behaviors and assign corresponding weights. Obtain and store log information of user behaviors through distributed log collection tools, and the log information of corresponding sub-behaviors below the weight threshold is filtered. Then, the log information of the retained sub-behaviors is processed in parallel through the utility function. The utility function establishes the mapping relationship between user interest degree and sub-behavior indicators. The corresponding log information of the sub-behaviors below the user interest degree threshold is deleted, and the log information of the user’s preferred sub-behaviors is retained, forming an optimized data source for recommendation results, and stored in the data cluster. This method can perform secondary filtering of the massive log information, respond to users’ current requirements and interesting information promptly, improving processing efficiency.