Google llc (20240338396). CONTEXTUAL ESTIMATION OF LINK INFORMATION GAIN simplified abstract
Contents
CONTEXTUAL ESTIMATION OF LINK INFORMATION GAIN
Organization Name
Inventor(s)
Pedro Gonnet Anders of Zurich (CH)
CONTEXTUAL ESTIMATION OF LINK INFORMATION GAIN - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338396 titled 'CONTEXTUAL ESTIMATION OF LINK INFORMATION GAIN
Simplified Explanation: The patent application describes techniques for determining an information gain score for documents of interest to the user and presenting information based on this score.
Key Features and Innovation:
- Determining information gain score for documents beyond previously viewed ones
- Applying machine learning models to generate information gain scores
- Providing documents to the user based on their potential information gain
Potential Applications: This technology can be applied in information retrieval systems, recommendation engines, and personalized content delivery platforms.
Problems Solved: This technology addresses the challenge of efficiently presenting relevant information to users based on their previous interactions with documents.
Benefits:
- Improved user experience by presenting highly relevant information
- Increased engagement and satisfaction with content
- Enhanced personalization of information delivery
Commercial Applications: Potential commercial applications include online content platforms, digital libraries, and knowledge management systems. This technology can help businesses enhance user engagement and drive revenue through targeted content delivery.
Prior Art: Researchers and patent searchers can explore prior art related to information retrieval, machine learning models, and personalized content delivery systems.
Frequently Updated Research: Stay informed about advancements in machine learning algorithms, natural language processing techniques, and user behavior analysis in information retrieval systems.
Questions about Information Gain Score: 1. How does the information gain score differ from traditional relevance ranking methods? 2. What are the implications of using machine learning models to determine information gain scores?
Original Abstract Submitted
techniques are described herein for determining an information gain score for one or more documents of interest to the user and present information from the documents based on the information gain score. an information gain score for a given document is indicative of additional information that is included in the document beyond information contained in documents that were previously viewed by the user. in some implementations, the information gain score may be determined for one or more documents by applying data from the documents across a machine learning model to generate an information gain score. based on the information gain scores of a set of documents, the documents can be provided to the user in a manner that reflects the likely information gain that can be attained by the user if the user were to view the documents.
