Personalized Feed Hash Filtering Reduces Compute Load
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Solution Overview
Problem
The challenge lies in efficiently determining which web documents are of interest to a user within a vast corpus, as existing methods require significant computing resources and may provide uninteresting content, leading to user disengagement.
Innovation Solution
A technique that utilizes user interactions, such as clicks and feedback, to determine web document interest, employing collaborative filtering and sim hash to reduce computational load and bias, and adjusts the content feed to maintain user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search algorithms are used to identify relevant web documents, then comprehensive search results can be provided, but significant computing resources are consumed
Solution Approach 1:
The system pre-computes and stores hash values for web documents before they are queried. When a user query arrives, the system performs a quick hash comparison rather than a full document analysis, significantly reducing the computing resources needed at query time while maintaining the ability to identify relevant documents
Solution Approach 2:
The patent introduces hash values as an intermediary representation of web documents. Instead of directly comparing full documents or using complex search algorithms on every query, the system uses these pre-computed hash values as a filtering mechanism to quickly identify potentially relevant documents before applying more sophisticated relevance assessment
2Measurement precision
If user interest is determined through extensive analysis, then accurate content recommendations can be provided, but computing resources and time are significantly consumed
Solution Approach 1:
The system pre-computes hash values for user profiles and web documents, and stores them in advance. When determining user interest, the system performs a rapid hash comparison rather than conducting extensive real-time analysis of user behavior and document content, significantly reducing processing time while maintaining measurement precision
Solution Approach 2:
The patent segments the user interest determination process into two stages: a fast filtering stage using pre-computed hash values to identify candidate documents, and a more detailed analysis stage only for those candidates. This segmentation allows the system to achieve accurate user interest determination without consuming excessive processing time on all documents
3Reliability
If comprehensive web document analysis is performed, then relevant content can be identified, but computing resources are excessively consumed
Solution Approach 1:
The system performs preliminary hashing of web documents and stores these hash values in advance. This pre-computation allows the system to quickly filter and identify relevant content when queries are received, avoiding the need to perform comprehensive analysis on every document for every query, thus improving resource efficiency while maintaining content relevance
Solution Approach 2:
The patent uses hash values as an intermediary layer between the query system and the full web document corpus. This intermediary enables efficient filtering and matching without requiring direct, resource-intensive comparison between queries and all documents, achieving both relevant content identification and improved productivity
Data Source
AI summary
A feature vector associated with a candidate web document is determined. A feature space associated with the feature vector is filtered. A density value associated with the candidate web document is determined using the filtered feature space. The candidate web document is ranked with respect to a plurality of other candidate web documents based on the determined density value. The candidate web document is provided in a content feed based on the ranking.