User Behavior Filtering for E-Commerce Recommendation Accuracy
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Solution Overview
Problem
E-commerce recommendation systems face challenges in accurately analyzing user preferences due to biases and fraudulent data in historical user behavior, leading to mismatched product recommendations and increased processing burdens from large volumes of user behavior data.
Innovation Solution
The system filters out counterfeit and abnormal data by comparing user behavior categories and frequencies with preset threshold values, conducts user preference analysis on optimized data, and provides targeted content recommendations, incorporating time-weighted analysis and deviation correction to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the recommendation system analyzes vast quantities of user behavior data to obtain user preference data, then the accuracy of user preference analysis may improve, but the processing speed of the recommendation system deteriorates
Solution Approach 1:
The patent segments user behavior data into different categories (normal behavior, abnormal behavior, fraudulent behavior) and processes them separately through filtering mechanisms. This segmentation allows the system to focus computational resources on analyzing valid user preferences while efficiently discarding irrelevant or malicious data, thereby improving processing speed without sacrificing accuracy.
Solution Approach 2:
The patent implements preliminary filtering of user behavior data before preference analysis. By pre-processing data to remove fraudulent and abnormal entries using threshold comparisons and statistical methods, the system prepares cleaned data in advance, which accelerates the subsequent preference analysis process while maintaining high accuracy.
2Loss of information
If the recommendation system uses historical user behavior data for analysis, then user preference data can be obtained, but the data may contain biases and fraudulent information that deteriorate recommendation accuracy
Solution Approach 1:
The patent extracts and removes fraudulent and abnormal data from the historical user behavior dataset through multiple filtering mechanisms. By separating valid user preferences from malicious or erroneous data, the system preserves complete user preference information while eliminating sources of inaccuracy, thus resolving the contradiction between data completeness and analysis accuracy.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor and evaluate user behavior data quality. Through statistical analysis and threshold comparisons, the system identifies patterns of fraudulent behavior and adjusts filtering criteria accordingly, creating a self-correcting system that maintains high preference analysis accuracy while preserving valid user preference data.
3Loss of information
If the recommendation system processes all user behavior data without filtering, then comprehensive user preferences may be captured, but the system complexity and processing burden increase
Solution Approach 1:
The patent applies different processing qualities to different portions of user behavior data based on their characteristics. Normal user behaviors receive detailed analysis, while suspicious or fraudulent data undergoes simplified filtering. This local differentiation reduces overall system complexity while preserving complete preference information from valid user actions.
Data Source
AI summary
Content recommendation includes: determining a plurality of user behavior categories pertaining to a plurality of user behaviors by a plurality of users within a period of time, and a plurality of user behavior frequencies corresponding to the plurality of user behavior categories; determining whether the number of user behavior categories exceeds a preset category threshold; in the event that the number of user behavior categories exceeds the preset threshold, excluding one or more user behavior categories such that the number of remaining user behavior categories does not exceed the preset threshold; determining, based at least on the remaining user behavior categories and corresponding user behavior frequencies, user preference data comprising frequency distribution information of a plurality of pieces of content information targeted by user behaviors in the remaining user behavior categories; and recommending content information to a user based on the user preference data.