18657447. CONTEXTUAL ESTIMATION OF LINK INFORMATION GAIN simplified abstract (Google LLC)
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 18657447 titled 'CONTEXTUAL ESTIMATION OF LINK INFORMATION GAIN
Abstract: 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.
- Key Features and Innovation:**
- Determining information gain scores for documents of interest - Presenting information based on the information gain score - Utilizing machine learning models to generate information gain scores - Providing documents to users based on potential information gain
- Potential Applications:**
- Personalized content recommendations - Enhanced information retrieval systems - Targeted advertising based on user interests
- Problems Solved:**
- Overwhelming users with irrelevant information - Improving user engagement with content - Enhancing user experience by providing relevant information
- Benefits:**
- Increased user satisfaction - More efficient information consumption - Higher engagement rates with content
- Commercial Applications:**
Potential commercial uses and market implications include: - Content recommendation platforms - Marketing and advertising targeting systems - Information retrieval tools for businesses
- Questions about the technology:**
1. How does the machine learning model determine the information gain score for documents? 2. What are the potential privacy implications of using information gain scores for personalized content recommendations?
- Frequently Updated Research:**
Stay updated on advancements in machine learning models for information gain scoring and personalized content recommendations.
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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.
