AI Points of Interest Identification via Click Volume Sequences
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Solution Overview
Problem
Existing AI systems fail to effectively identify timely points of interest for users based on changing user behavior, which limits personalized and timely information delivery.
Innovation Solution
An AI method that processes search click information within a predetermined time period to select entries with high click volumes, segment click volume sequences based on trends, and categorize entries as points of interest using a pre-trained classification model, ultimately generating a set of points of interest for targeted information processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional AI systems are used to identify points of interest, then the system structure is simple, but the system fails to effectively identify timely points of interest based on changing user behavior
Solution Approach 1:
The patent segments the identification process into multiple specialized modules: a click volume sequence generation module that processes raw search data, a trend analysis module that identifies changing patterns, and a classification module that categorizes points of interest. This segmentation allows each module to specialize in one aspect of the complex identification task, improving overall accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system dynamically adapts to changing user behavior by continuously processing new search click data and updating point of interest identification in real-time. The trend analysis component detects dynamic changes in click patterns, and the system adjusts its identification criteria based on observed behavioral changes, enabling accurate identification of timely points of interest rather than relying on static thresholds.
2Loss of time
If real-time processing of search click information is implemented, then timely points of interest can be identified, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential features from raw search click data - specifically click volumes, time stamps, and entry identifiers - to form click volume sequences. By extracting only these critical elements rather than processing complete search queries and user profiles, the system achieves real-time processing capability with reduced computational overhead while maintaining the ability to identify timely points of interest.
Solution Approach 2:
The system processes click volume data at aggregated levels rather than individual query levels, applying partial processing to the most relevant features. By focusing computational resources on analyzing click volume trends and patterns rather than processing every detail of search behavior, the system achieves timely identification with optimized resource utilization.
3Measurement precision
If comprehensive analysis of user behavior is performed, then accurate points of interest can be identified, but the complexity of data processing increases
Solution Approach 1:
The patent applies different processing strategies to different aspects of user behavior data. Click volume information is processed to identify popularity trends, temporal patterns are analyzed separately to detect timeliness, and category information is processed to understand user preferences. This local quality approach allows comprehensive analysis of user behavior while maintaining manageable processing complexity through specialized handling of each data dimension.
Solution Approach 2:
The system transforms raw click data into click volume sequences as an intermediate representation, changing the parameter form from discrete search events to continuous temporal sequences. This parameter transformation simplifies the analysis of user behavior patterns by converting complex behavioral data into a standardized format that can be processed using sequence analysis techniques, reducing overall processing complexity while maintaining identification accuracy.
Data Source
AI summary
An artificial intelligence based method and apparatus for processing information. A specific embodiment of the method includes: acquiring search click information recorded within a predetermined time period; generating a candidate entry set by selecting, from the search click information, entries having click volumes exceeding a click volume threshold within a preset unit time period; forming, for each candidate entry in the candidate entry set, a click volume sequence according to a chronological order of each of the click volumes corresponding to the candidate entry in the predetermined time period; determining, based on click volume sequences, categories of the candidate entries respectively corresponding to click volume sequences; and determining candidate entries having the categories being a preset category as points of interest to generate a set of points of interest.