Charity Recommendation via Semantic Embedding and Reinforcement Learning
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Solution Overview
Problem
The challenge lies in automating charitable giving triggered by content consumption, as existing systems face difficulties in efficiently identifying relevant charities and processing large volumes of natural language text for semantic similarity analysis, leading to issues like computational scaling and data overfitting.
Innovation Solution
A content classification system utilizing machine-learning and natural language modeling to identify relevant charities by generating semantic representations of text, employing reinforcement learning to refine charity queries, and using encoder-decoder architectures to filter and summarize charity descriptions for accurate similarity comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated charitable giving systems process large volumes of natural language text for semantic similarity analysis, then charity identification accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system segments the charity matching process into distinct stages: text preprocessing, semantic representation generation, similarity computation, and charity ranking. Each stage processes data independently and passes results to the next stage, reducing overall computational complexity while maintaining identification accuracy.
Solution Approach 2:
The patent introduces intermediary components including pre-trained language models that generate semantic representations as intermediate data structures, and similarity computation modules that bridge content text and charity descriptions. These intermediaries simplify the complex task of direct text comparison.
2Measurement precision
If the system analyzes complete charity descriptions for semantic similarity, then matching accuracy improves, but processing efficiency decreases
Solution Approach 1:
The system extracts key semantic features and critical phrases from complete charity descriptions using natural language processing techniques. By taking out only the most relevant semantic elements rather than processing entire descriptions, the system maintains matching accuracy while significantly improving processing efficiency.
Solution Approach 2:
The patent applies partial action by processing only the portions of charity descriptions that are most relevant to the content being matched. The system identifies and processes key sentences or phrases that contain essential semantic information, avoiding unnecessary processing of redundant or less relevant text portions.
3Reliability
If the system processes all content simultaneously for charity identification, then comprehensive matching improves, but computational resource requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-processing content and charity descriptions before main matching operations. This includes generating semantic representations in advance, filtering obviously unrelated content, and organizing data structures for efficient access. These preliminary steps enable comprehensive matching while reducing peak computational resource requirements.
Solution Approach 2:
The patent implements periodic processing where content is analyzed in batches or cycles rather than all at once. The system processes content streams periodically, maintaining comprehensive matching capabilities while allowing computational resources to be managed in controlled intervals, reducing overall energy consumption.
Data Source
AI summary
The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.