Document Recommendation via Named Entity Semantic Analysis
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Solution Overview
Problem
Conventional document recommendation approaches fail to recommend documents that use synonyms, hypernyms, or hyponyms of words or phrases found in read documents, and miss connections between documents related to the same overarching idea, even if they don't explicitly mention it.
Innovation Solution
A facility that monitors user interactions with documents, semantically determines document subjects by analyzing named entities and their relationships, and recommends documents based on high interaction intensity and frequency across a user population, even if they don't contain matching keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional keyword-based recommendation approaches are used, then implementation simplicity is maintained, but recommendation accuracy deteriorates because synonyms, hypernym, and hyponym relationships are not captured
Solution Approach 1:
The patent introduces named entity recognition and semantic relationship analysis as intermediary layers between the document corpus and recommendation engine. By identifying named entities (persons, organizations, locations, etc.) and their relationships in documents, the system creates a semantic bridge that connects documents through meaning rather than just keywords, thereby improving recommendation accuracy while managing complexity through modular processing steps
Solution Approach 2:
The system transforms the recommendation approach from keyword-based matching to semantics-based matching by changing the fundamental parameter from text string comparison to named entity relationship analysis. This parameter change enables the system to capture synonym,hypernym, and hyponym relationships, significantly improving recommendation precision despite increased computational requirements
2Measurement precision
If semantic analysis of named entities is performed for all documents, then recommendation quality improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing documents to identify and extract named entities and their relationships before the recommendation process. This advance processing creates a reusable semantic structure that can be queried efficiently during recommendation generation, avoiding the need to perform full semantic analysis on-demand and thus reducing processing time for actual recommendations
Solution Approach 2:
The system segments the document processing into distinct stages: named entity identification, relationship extraction, and recommendation generation. By dividing the complex semantic analysis task into manageable segments, the system can optimize each stage independently and cache intermediate results, reducing overall processing time while maintaining high recommendation precision
3Measurement precision
If interaction intensity and frequency metrics are collected for all users, then recommendation relevance improves, but data storage requirements increase
Solution Approach 1:
The patent extracts only the essential interaction metrics (intensity and frequency) needed for recommendation relevance from the full user interaction data. By selectively extracting these key parameters rather than storing complete interaction logs, the system maintains high recommendation relevance while significantly reducing data storage requirements through focused data collection
Data Source
AI summary
In some examples, a facility chooses documents for recommendation to people interested in a distinguished subject. The facility accesses a data resource indicating, for each of a plurality of documents, (1) a subject semantically determined for the document, (2) a number of people who have interacted with the document, and (3) an aggregate level of intensity with which those people have interacted with the document. Based on contents of the data structure, the facility identifies one or more documents (1) for which the distinguished subject has been semantically determined, and (2) with which a relatively large number of people have interacted and/or with which people have interacted with a high aggregate level of intensity. The facility transmits to another computing system data configured to cause the first computing system to present a recommendation for some or all of the identified documents.