Dynamic Preference Graph for Content Ranking
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Solution Overview
Problem
Conventional information retrieval systems struggle to accurately identify and present relevant information to users due to limitations in specifying user preferences, leading to user overload and inefficiency in finding desired information.
Innovation Solution
A computer-implemented method and system for calculating a ranking of items based on user preferences, which includes receiving first-order and second-order user preferences and using a preference graph to determine the ranking of items, thereby presenting the most relevant information to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional search systems present all potentially relevant results to users, then the user can access comprehensive information, but the user becomes overwhelmed and fails to locate desired information efficiently
Solution Approach 1:
The system segments search results into multiple ranked lists based on different preference dimensions (e.g., price, color, reviews). Instead of presenting a single flat list of all results, the system divides them into organized groups that match user preferences, making the information more manageable and easier to navigate.
Solution Approach 2:
The system incorporates user feedback by receiving explicit preference indications and using them to dynamically adjust result ranking. The preference graph is updated based on user interactions, allowing the system to learn from user behavior and improve result relevance over time, thereby increasing information retrieval efficiency.
2Device complexity
If conventional systems use rigid sorting models, then the system structure remains simple, but the flexibility of preference specification is limited
Solution Approach 1:
The system transitions from static sorting models to dynamic preference-based ranking. The preference graph is constructed and updated dynamically based on user inputs and interactions, allowing the system to adapt to different preference specifications without changing its fundamental architecture. This enables flexible preference handling while maintaining system simplicity.
Solution Approach 2:
The preference graph data structure serves multiple functions: it stores user preferences, processes ranking queries, and generates personalized result lists. This universal representation allows the system to handle various preference types (first-order and second-order) and complex preference combinations without requiring separate processing mechanisms for each case.
3Adaptability or versatility
If systems handle inconsistent preferences, then the system can process diverse user inputs, but accurate ranking becomes difficult to achieve
Solution Approach 1:
The system converts inconsistent preferences into useful ranking signals by using the preference graph to identify and resolve conflicts. Inconsistent preferences are processed through the graph structure, which can detect cycles and contradictions, and transform them into adjusted rankings that respect user intentions as much as possible. This allows the system to handle diverse and inconsistent inputs while maintaining reasonable ranking accuracy.
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.