Personalization Platform Rule Configuration for Output Alignment
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Solution Overview
Problem
Existing content personalization models often generate result sets that do not align with target objectives, posing challenges for system designers and users.
Innovation Solution
A personalization platform that interprets user behavior and attributes to build optimized predictive models, utilizing filters that can be configured and prioritized to adjust system output and satisfy desired result set criteria, applicable in various environments such as email, mobile, and applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing content personalization models are used, then content can be recommended based on user behavior, but the result sets do not align with target objectives
Solution Approach 1:
The system incorporates a feedback mechanism where business users can review and provide feedback on personalization results. The feedback component allows users to indicate whether recommended content meets target objectives, and this feedback is used to iteratively improve the predictive models and filter criteria, thereby aligning output with desired results over time
Solution Approach 2:
The system allows dynamic adjustment of filter criteria and model parameters through the rule configuration interface. Business users can modify filter settings, priority weights, and selection thresholds to optimize personalization results for different target objectives, enabling precise control over output alignment
2Measurement precision
If complex personalization algorithms are implemented, then more accurate predictive models can be built, but the system becomes difficult to configure and control
Solution Approach 1:
The system segments the personalization process into distinct configurable components: filter criteria, priority levels, selection thresholds, and feedback mechanisms. Each component can be independently configured through the rule interface, allowing business users to control complex personalization logic without needing to understand or program the underlying algorithms
Solution Approach 2:
The rule configuration interface acts as an intermediary between business users and the complex personalization algorithms. Users interact with simplified, business-friendly configuration options rather than raw algorithmic parameters, while the system translates these high-level rules into executable personalization logic
3Manufacturing precision
If multiple filter criteria are applied to personalization results, then result quality can be improved, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary filtering and sorting of content based on predictive models before applying business-specific filter criteria. This pre-processing organizes content in advance, allowing subsequent filter application to operate on a reduced, pre-sorted dataset, thereby reducing overall processing time while maintaining result quality
Solution Approach 2:
The filter criteria and their priorities are dynamically adjustable based on business needs and performance. The system can adaptively weight different filters and adjust processing depth based on result quality metrics, optimizing the balance between processing time and result quality for different scenarios
Data Source
AI summary
Processes and apparatuses for content personalization are provided, providing for rule configuration. Content personalization systems interpret user behavior and attributes along with the content users are interacting with, to build optimized predictive models of what content the user may want to see next. Those predictive models can be utilized to personalize content in one or more environments, including email, mobile and applications. Rules include filters applied to the predictive model output and/or the overall system output. Filters can be prioritized and iteratively applied or removed to adjust system output to satisfy desired result set criteria.