Information Gain Scoring for Document Selection
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Solution Overview
Problem
When a user is presented with multiple documents sharing a common topic, many documents contain similar information, leading to user disinterest in viewing additional documents after already viewing similar information in previous documents.
Innovation Solution
Determining an information gain score for each document to assess the additional information it provides beyond what has already been presented to the user, and selectively presenting information from documents with higher information gain scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple documents sharing a topic are presented to the user, then the completeness of information retrieval is improved, but the user interest and engagement deteriorate due to redundancy
Solution Approach 1:
The system changes the parameter of document selection from uniform random selection to selective selection based on information gain scores. Each document is assigned a score reflecting its unique information value beyond previously presented documents, and documents are selected based on these scores to maximize information diversity while maintaining user engagement.
2Quantity of substance
If all documents are presented to ensure comprehensive information, then the information completeness is improved, but the time required for information retrieval increases
Solution Approach 1:
The system applies partial action by selecting only the subset of documents that provide maximum information gain rather than presenting all available documents. The information gain scoring mechanism identifies and presents only the most valuable documents that contribute new information, avoiding redundant presentations and reducing time consumption while maintaining information completeness.
3Ease of operation
If documents with high information gain are selectively presented, then the user engagement is improved, but the complexity of the selection process increases
Solution Approach 1:
The system performs preliminary action by pre-calculating information gain scores for documents before presenting them to the user. The machine learning model processes and scores documents in advance based on their information value relative to previously presented documents, so that the actual user interaction involves only selecting pre-scored documents rather than evaluating each document's value in real-time, reducing the perceived complexity.
4Quantity of substance
If redundant information is presented to ensure thorough coverage, then the information completeness is improved, but the efficiency of information retrieval deteriorates
Solution Approach 1:
The system extracts and removes redundant information from the document set before presentation. The information gain scoring mechanism identifies documents that provide duplicate or overlapping information compared to previously presented documents and excludes them from the selection, retaining only documents that contribute unique information value, thereby improving retrieval efficiency while maintaining completeness.
Data Source
AI summary
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.