Personalized Search Ranking via Cross-Space Data Segmentation
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Solution Overview
Problem
The rapid growth of information across public, semi-private, and private spaces leads to information overload, with existing technologies failing to efficiently organize and retrieve relevant information due to segregation of data sources, resulting in a cumbersome user experience.
Innovation Solution
A person-centric INDEX system that cross-links data from various spaces, including private, semi-private, and public domains, to create a unified, dynamic information universe relevant to the individual, providing search query suggestions and automating task completion by integrating relevant information from diverse sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from multiple spaces (public, semi-private, private) are collected to provide comprehensive information, then information completeness is improved, but system complexity increases due to the need to manage and integrate diverse data sources
Solution Approach 1:
The patent segments the information retrieval system into separate modules: a suggestion generation module that handles personalized recommendations, a search module that handles query-based retrieval, and a ranking module that integrates results. Each module processes specific data types from different spaces independently before combining them, reducing overall system complexity while maintaining comprehensive information coverage.
Solution Approach 2:
The patent introduces a ranking module as an intermediary that receives suggestions from multiple sources (personalized suggestions from private space data, generic suggestions from public space data) and integrates them into a unified ranked list. This intermediary component simplifies the complexity of integrating diverse data sources by providing a single interface for result consolidation and presentation.
2Measurement precision
If personalized search suggestions are generated using private data to improve relevance, then search accuracy is improved, but user privacy concerns increase
Solution Approach 1:
The patent applies partial action by generating personalized suggestions based on limited subsets of private data (such as search history, contacts, emails) rather than requiring access to all personal information. The system selectively uses only the necessary data elements needed for accurate suggestions, minimizing privacy exposure while maintaining search accuracy. Generic suggestions from public data serve as a backup when personal data is insufficient or sensitive.
Solution Approach 2:
The patent changes the parameter of data availability by dynamically adjusting the degree of personalization based on what data is available and appropriate. When private data is available, the system uses it for personalized suggestions; when it's not available or overly sensitive, the system switches to generic suggestions from public data. This flexible parameter adjustment allows the system to maintain search accuracy while adapting to privacy constraints.
3Adaptability or versatility
If search suggestions integrate both personalized and generic results to improve comprehensiveness, then information coverage is improved, but processing time increases due to the need to rank and integrate multiple suggestion sets
Solution Approach 1:
The patent applies preliminary action by pre-processing and ranking personalized suggestions separately from generic suggestions before integration. The system generates and ranks personalized suggestions based on private data, and generates generic suggestions based on public data, then combines these pre-ranked lists into a final integrated ranking. This preliminary separation and ranking reduces the processing time required for final integration by avoiding the need to re-rank all suggestions from scratch.
Data Source
AI summary
The present teaching, which includes methods, systems and computer-readable media, relates to providing query suggestions based on a number of data sources that include person's personal data and non-personal data. The disclosed techniques may include receiving an input from a person, obtaining a first set of suggestions based on a person corpus derived from at least one data source private to the person, obtaining a second set of suggestions based on information from an additional data source, ranking the first and second sets of suggestions to generate a ranked list of suggestions, and presenting at least some of the ranked suggestions.