Interest Burst Detection Using Overlapping Time Windows
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Solution Overview
Problem
Identifying interest burst items in real-time is challenging due to the difficulty in distinguishing genuine interest spikes from artificially generated spikes, and existing methods are costly and inefficient for dynamic list generation.
Innovation Solution
Implementing a system that maintains overlapping time windows for interest-action counts, uses lifetime and window counts to identify interest burst candidates, and employs spam detection techniques to filter out artificially generated interest, with a scoring system to rank items based on temporal interest increases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real-time identification of interest burst items is implemented, then the timeliness and relevance of the interest burst list is improved, but the computational cost and complexity increases
Solution Approach 1:
The patent divides the computational task into segments by maintaining multiple overlapping time windows (e.g., 1-hour, 6-hour, 12-hour windows) that slide through the data stream. Each window independently tracks interest-action counts, allowing the system to identify interest bursts at different time scales without requiring a complete reanalysis of all historical data, thus reducing overall computational complexity while maintaining real-time detection capability.
Solution Approach 2:
The system performs preliminary actions by pre-computing and maintaining interest-action counts for each item across multiple time windows in advance. When identifying interest bursts, the system only needs to compare pre-computed counts from different windows rather than processing raw data, significantly reducing the computational burden during real-time identification while preserving timeliness.
2Reliability
If spam detection techniques are employed to filter artificially generated interest, then the reliability of interest burst identification is improved, but the processing time and computational overhead increases
Solution Approach 1:
The patent changes parameters by introducing a threshold-based spam detection mechanism that compares interest-action counts across overlapping time windows. Items showing abnormal spikes (e.g., sudden increase in interest actions beyond a calculated threshold) are flagged as potential spam. This parameter-based approach enables reliable spam filtering without requiring complex analysis, thus maintaining processing efficiency while improving detection accuracy.
3Measurement precision
If overlapping time windows are maintained for interest-action counts, then the ability to detect recent interest bursts is improved, but the memory requirements and data storage increases
Solution Approach 1:
The patent applies the nesting principle by organizing time windows in a hierarchical structure where smaller windows are nested within larger windows. For example, a 1-hour window is contained within a 6-hour window, which is contained within a 12-hour window. This nested structure allows the system to maintain multiple time scales using shared data structures, reducing memory requirements compared to maintaining completely separate windows for each time scale while preserving precise detection capability.
Data Source
AI summary
Techniques are described for identifying items that have recently undergone an interest burst. Items that have recently undergone an interest burst are identified by comparing how many interest-actions have been performed on the items during a current time window against how many interest-actions have been performed on the items historically. Various tests are performed to rule out candidates that are not likely to be of interest to other users. In addition, various spam detection techniques are described for reducing the possibility that the items that are listed as interest burst items are listed because of spam.