User Behavior Data Processing with Time Attenuation and Periodicity Factors
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Solution Overview
Problem
Search engines fail to effectively capture changes in user interests due to treating all historical data equally, leading to suboptimal search result rankings that do not accurately reflect user preferences.
Innovation Solution
Assigning different degrees of importance to user behavior data based on time attenuation and periodicity similarities, using timeliness and periodicity factors to adjust the data, thereby optimizing search result rankings through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all historical data is treated equally in machine learning, then the system uses complete historical information for prediction, but the timeliness of user behavior data deteriorates and user interest changes cannot be captured effectively
Solution Approach 1:
The patent applies parameter changes by introducing time-based weighting parameters (timeliness factors and periodicity factors) that dynamically adjust the importance of historical data points. These parameters transform the static treatment of all historical data into a dynamic system where data from different time periods and patterns receive different weights, thereby capturing user interest changes while preserving relevant historical information.
2Device complexity
If uniform weighting is applied to all historical behavior logs, then the processing complexity is reduced, but the accuracy of capturing user preferences deteriorates
Solution Approach 1:
The patent implements local quality by assigning different weights to different historical behavior logs based on their temporal characteristics and periodicity patterns. Instead of uniform processing, the system identifies specific data points with higher relevance (such as recent behaviors or periodic patterns) and gives them greater importance, thereby improving user preference capture accuracy while maintaining manageable processing complexity through targeted differentiation.
Data Source
AI summary
The present disclosure provides methods and apparatuses for processing user behavior data. One exemplary processing method comprises: acquiring behavior data of a user, and a time at which the behavior data is generated; determining at least one of a timeliness factor and a periodicity factor corresponding to the behavior data according to the time at which the behavior data is generated and a current time; and adjusting the behavior data according to the at least one of the timeliness factor and the periodicity factor. With the processing methods provided by the present disclosure, the timeliness of the user behavior data can be improved. The preference and interest of the user can be acquired more effectively. That way, tailor search results can be provided to meet the demand of the user, thereby improving user experience.