Topic Source Identification via User Interaction Weighting
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Solution Overview
Problem
Current internet search engines and social networking services lack effective methods to identify and prioritize high-quality information sources based on user interactions, leading to suboptimal content delivery and search result rankings.
Innovation Solution
The method involves analyzing user interaction data across various services to associate topics with information sources, calculating weights for document-text pairs, and using these weights to determine the topics for which a source produces high-quality content, thereby enhancing content delivery and search result rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user interaction data is collected and analyzed to identify high-quality sources, then content delivery quality improves, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of user interaction data to identify and categorize high-quality sources before they are needed for content delivery. By pre-computing source reputations and topic associations, the system avoids complex real-time evaluations during actual content delivery operations.
Solution Approach 2:
The patent introduces an intermediary layer that processes and pre-computes source quality metrics and topic associations. This intermediary component separates the complex analysis operations from the content delivery function, allowing the delivery system to operate simpler while still benefiting from high-quality source identification.
2Measurement precision
If sources are identified based on user interaction data, then search result rankings improve, but data collection requirements increase
Solution Approach 1:
The system extracts and focuses on specific, high-value user interaction signals (such as selections, views, and engagement metrics) rather than collecting all possible user data. By selectively extracting only the relevant interaction types needed for source quality assessment, the system achieves accurate rankings with reduced data collection requirements.
Solution Approach 2:
The patent applies different data collection and analysis strategies to different sources and topics based on their local characteristics. High-quality sources with sufficient interaction data receive more precise analysis, while lesser-known sources are evaluated using available local data patterns, allowing accurate rankings without requiring uniform extensive data collection across all sources.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for collecting user interaction data of a plurality of users for each of a first plurality of document-text pairs, wherein the user interaction data is collected for the document-text pair from a respective service for which the respective text of the document-text pair was selected. A respective weight is calculated for each of the first plurality of document-text pairs based on, at least, the collected user interaction data for the document-text pair. One or more topics are associated with one or more of the sources based on, at least, the respective weights associated with a plurality of first document-text pairs that are associated with the source.