Preference Graph Ranking for Information Retrieval
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Solution Overview
Problem
Conventional information retrieval systems fail to accurately identify and present relevant information to users due to limitations in specifying and integrating user preferences, leading to user overload and inefficiency in finding desired content.
Innovation Solution
A computer-implemented method and system that calculates a ranking of items based on a preference graph representing user preferences, including first-order and second-order preferences, to identify and output relevant items to users, allowing for flexible and accurate preference specification and integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional information retrieval systems present all potentially relevant results to users, then users have access to comprehensive information, but users become overwhelmed and fail to locate information of interest efficiently
Solution Approach 1:
The system automatically performs preference analysis and result ranking without requiring manual user intervention. The server autonomously processes user preferences, analyzes content relevance, and orders results according to user-specific criteria, eliminating the need for users to manually filter through overwhelming quantities of information.
Solution Approach 2:
The system dynamically changes the parameter of information presentation by transforming unstructured or semi-structured content into structured formats with extracted entities, attributes, and relationships. This parameter transformation enables preference-based filtering and ranking, converting comprehensive but unordered information into tailored, ordered results.
2Measurement precision
If conventional search systems provide explicit search queries, then users can indicate their information needs, but the queries are insufficient to accurately identify appropriate content from available content
Solution Approach 1:
The system performs preliminary preference specification by automatically analyzing user profiles, historical behavior, and contextual information before content delivery. This preliminary action establishes detailed preference parameters in advance, enabling precise content matching without requiring complex real-time user input.
Solution Approach 2:
The system introduces an intermediary preference analysis layer between the user's simple query and the content database. This intermediary component translates basic queries into detailed preference specifications by incorporating user profiles, contextual data, and automated analysis, thereby bridging the gap between simple user input and precise content identification.
3Adaptability or versatility
If conventional systems limit ways to specify user preferences, then system complexity is reduced, but the utility and accuracy of preference-based information retrieval is limited
Solution Approach 1:
The system implements a universal preference representation framework that handles multiple preference types (explicit, implicit, hierarchical, contextual) through a unified model. This multi-functional approach allows diverse preference specifications to be processed through the same analytical engine, increasing adaptability without proportionally increasing complexity.
Solution Approach 2:
The system transforms various preference specifications into standardized parameters through automated analysis. Different input formats (user profiles, behavioral data, explicit preferences) are converted into unified preference parameters that can be systematically applied to content ranking, enabling versatile preference handling with manageable complexity.
Data Source
AI summary
Techniques for presenting content to users. The techniques include: obtaining user context information including a first keyword; identifying, based on the first keyword, a first attribute and a second attribute among the plurality of attributes, the first attribute being a characteristic of the first keyword and the second attribute being another characteristic of the first keyword; obtaining, based on the user context information, at least one second-order user preference among attributes in the plurality of attributes including a preference between the first attribute and the second attribute; identifying a set of content items among the plurality of content items based on the first attribute and the second attribute; determining a ranking of content items in the set of content items based on the at least one second-order user preference; and presenting content items to the user in accordance with the ranking.