Streaming Event Ranking Model for Commerce Interface
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Solution Overview
Problem
Existing commerce user interfaces often provide incomplete or irrelevant information due to reliance on predicted consumer activity and predefined categories, leading to inefficient use of computer resources and inaccurate search results, as they fail to consider actual user activity and behavior.
Innovation Solution
The implementation of data processing techniques that analyze streaming interaction events to generate a ranking model balancing engagement, exposure, and relevancy scores, allowing for real-time adjustment and optimization of information displays, thereby providing personalized and accurate search results based on actual user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predicted consumer activity and predefined categories are used for information ranking, then system complexity is reduced and processing is simplified, but information retrieval accuracy and relevance deteriorate
Solution Approach 1:
The patent implements dynamic information ranking that adapts to actual user interactions in real-time. The system continuously updates ranking models based on streaming event data, transforming static predefined categories into dynamic, context-aware rankings that respond to user behavior patterns, thereby improving accuracy without requiring overly complex manual configuration
Solution Approach 2:
The system incorporates feedback loops where user interactions (clicks, views, purchases) are captured as streaming events and fed back into the ranking model. This continuous feedback mechanism allows the system to learn from actual consumer activity and automatically adjust rankings, resolving the contradiction by making the system adaptive rather than statically complex
2Measurement precision
If actual user activity data is collected and processed in real-time, then information relevancy and personalization improve, but data processing complexity and resource consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and structuring event data as it arrives, creating normalized event streams that are ready for immediate analysis. Event schemas and data pipelines are established in advance, allowing real-time processing without ad-hoc complexity when queries are executed
Solution Approach 2:
The patent introduces intermediary components such as event stream processors and ranking models that act as mediators between raw user interaction data and final rankings. These intermediaries simplify the processing architecture by breaking down complex real-time analysis into manageable stages, each handling specific transformation tasks
3Speed
If predefined ranking categories are used, then processing speed is maintained and system responsiveness is preserved, but adaptability to actual user behavior deteriorates
Solution Approach 1:
The system dynamically changes ranking parameters based on actual user behavior patterns detected in streaming events. Instead of fixed predefined categories, the ranking model adjusts weights, thresholds, and prioritization criteria in real-time according to observed user interactions, maintaining speed through parameter optimization rather than structural reconfiguration
Data Source
AI summary
Techniques and system configurations for generating rankings and ranking models for information based on streaming event data are disclosed herein. In an example, electronic operations used for ranking and ordering information provided in commerce user interface based on streaming events include: processing streaming events representing user interaction in a commerce user interface; producing a ranking model from the events for a subsequent output within the commerce user interface; and generating the subsequent output of the information set via the commerce user interface, using the ranking model, where respective items of information are ordered among each other based on an engagement score, an exposure score, or a relevancy score. In a further example, the exposure score is used as a weight to the engagement score, relative to the relevancy score. With this technique, a balanced approach for presenting and organizing data relevancy may be offered in user interfaces.