Decentralized Content Recommendation System Using Probabilistic Feedback
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Solution Overview
Problem
Current content recommendation systems are centralized, require extensive web crawling, and lack personalization, often necessitating users to explicitly state their interests or tag content, which can be cumbersome and inefficient.
Innovation Solution
A decentralized method where users interact with a local content recommendation system using 'next' and 'select' buttons, updating probabilities based on user feedback to optimize content selection, reducing the need for explicit interest declaration and web crawling, and leveraging probabilistic rules to enhance user satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized recommendation system crawls the web periodically to discover and analyze content types, then content awareness and personalization are improved, but system complexity and resource requirements increase significantly
Solution Approach 1:
The system allows users to self-serve by directly submitting queries and receiving personalized recommendations without requiring the system to actively crawl and analyze all web content. The user-driven query mechanism replaces the system-driven content discovery approach.
Solution Approach 2:
The patent extracts only the necessary content information from user queries rather than comprehensively crawling and analyzing all web content. This selective extraction approach reduces system complexity while maintaining content awareness for personalized recommendations.
2Measurement precision
If users explicitly tag content or declare interests, then personalization accuracy is improved, but ease of operation deteriorates due to the cumbersome process
Solution Approach 1:
The system performs preliminary actions by proactively analyzing user queries and automatically inferring interests without requiring users to explicitly tag content or declare preferences. This eliminates the cumbersome tagging process while maintaining personalization accuracy.
Solution Approach 2:
The system uses feedback from user queries to automatically adjust and refine personalized recommendations. By analyzing what users search for, the system infers their interests and improves personalization accuracy without requiring explicit user input beyond the initial query.
3Adaptability or versatility
If a centralized server aggregates and stores user preferences, then personalization capability is improved, but loss of time and resource efficiency worsen due to extensive data processing
Solution Approach 1:
The patent segments the recommendation system into distributed components that process user queries locally rather than centralizing all data processing on a single server. This segmentation reduces the time and resources required for data aggregation and processing while maintaining personalization capability.
Solution Approach 2:
The system introduces an intermediary mechanism that processes user queries and generates recommendations without requiring extensive centralized data aggregation. This intermediary approach reduces data processing time by avoiding the bottleneck of centralized server operations.
4Quantity of substance
If the system crawls and stores extensive web content information, then content coverage is improved, but quantity of substance and storage requirements increase
Solution Approach 1:
The system performs partial action by processing only the specific content information needed to answer user queries rather than comprehensively crawling and storing all web content. This selective approach maintains content coverage for relevant queries while reducing storage requirements.
Data Source
AI summary
A method for recommending content items to a user is provided. It includes: (i) receiving one of at least an acceptance input and a rejection input from a user in relation to content presented to the user; (ii) in response to an acceptance input, rendering the presented content, or in response to a rejection input, selecting fresh content for presentation; and, (iii) repeating steps (i) and (ii) until a acceptance input is received. Content is selected in dependence on a associated probability associated with that content. The probability is increased in response to an acceptance input, the increase being determined in part on a measure of a predicted reduction in user satisfaction that would be associated with an additional rejection input.