Persona Model Application Engine for Search Personalization
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Solution Overview
Problem
Current search systems lack the ability to provide personalized and context-specific information to users, as they primarily rely on pre-existing web page data without the capability to generate new information based on user context or preferences.
Innovation Solution
Incorporating a model selection and application engine into the search stack that allows for the selection and application of persona-based models, which define characteristics and computations to generate information consistent with user context, such as individual or group preferences, interests, or needs, by using declarative statements and meta tags for indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional search engine using page index is used, then search results can be obtained quickly, but the information is not personalized and cannot generate new data based on user context
Solution Approach 1:
The search system is segmented into multiple independent components: a traditional search engine for快速获取 results, and a separate model application engine for personalization. The model application engine receives search results and user context, applies appropriate persona models, and generates personalized information without blocking the main search flow.
Solution Approach 2:
A model application engine is introduced as an intermediary component between the traditional search engine and the user. This intermediary receives raw search results, applies persona-based models to generate personalized information, and returns enhanced results to the user, thereby adding personalization capability without fundamentally changing the core search engine.
2Reliability
If persona models are applied to generate personalized information, then user satisfaction and relevance improve, but system complexity increases
Solution Approach 1:
The model application engine is designed as a universal component that can handle multiple types of persona models (e.g., expert personas, consumer personas, regional personas) through a single interface. It accepts various input formats and produces standardized personalized outputs, reducing the need for separate processing pipelines for different model types.
Solution Approach 2:
The system uses parameter-based model selection where different persona models are selected and configured based on user context parameters such as location, interests, and query type. This allows the same engine structure to handle diverse personalization scenarios by changing model parameters rather than adding complex decision logic.
3Adaptability or versatility
If multiple persona models are made available to users, then users can find information matching their specific needs, but the interface complexity and user burden increase
Solution Approach 1:
The model selection interface is designed to be dynamic rather than static. Instead of presenting users with a fixed list of persona models to choose from, the system dynamically determines and presents only the most relevant models based on the user's current context, query, and profile, reducing cognitive load while maintaining versatility.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with search results and persona selections are used to automatically refine future model recommendations. This feedback loop allows the system to learn user preferences over time, presenting increasingly accurate personalized options without requiring users to manually navigate complex selection interfaces.
Data Source
AI summary
A data search and retrieval system that, in response to a search query, applies a model to search results to generate information to be returned to a user. The model may define characteristics of a persona such that the application of the model generates information consistent with the persona. A persona may be an individual person or a group of people having shared characteristics. A user may be presented with a list of identifiers of available models of personae. When the user selects a model of a particular persona to be applied, the information generated in return to the user may be consistent with the characteristics associated with the selected persona.