Semantic Data File Vector Mapping for Music Recommendation
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Solution Overview
Problem
Current AI systems fail to effectively identify and recommend music or other media based on semantically perceived similarities, leading to inconsistent results due to their reliance on absolute similarities rather than subjective user experiences, and struggle with new artists or content due to lack of user data, resulting in poor user experience and limited exposure for new music.
Innovation Solution
A method and system that processes data files to extract properties, calculate file vectors representing semantic properties, and compare them to candidate files in a database to recommend semantically close matches, using a trained artificial neural network to map property similarities to semantic similarities, enabling personalized and effective music recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing AI systems use absolute similarity metrics to identify similar music files, then the system can process and compare files objectively, but the results do not align with human subjective perception of musical similarity
Solution Approach 1:
The patent transforms the similarity measurement from absolute objective parameters (genre, tempo, key) to subjective perceptual parameters by training a neural network to map property space distances to semantic space distances. This parameter transformation enables the system to measure similarity in a way that aligns with human perception while maintaining computational objectivity.
Solution Approach 2:
The patent introduces a trained neural network as an intermediary between the objective property extraction and the subjective similarity assessment. This intermediary model learns the mapping from measurable file properties to perceived semantic similarity, resolving the contradiction between objective measurement and subjective perception.
2Adaptability or versatility
If AI systems rely on user data and interaction history to make recommendations, then personalization improves, but new artists and content receive no exposure due to lack of user data
Solution Approach 1:
The patent performs preliminary semantic embedding of all music files during an offline preprocessing stage, creating property vectors and semantic representations before any user interactions occur. This preliminary action enables the system to provide reliable recommendations for new artists immediately, without requiring user data, while still supporting personalized recommendations as user data accumulates.
3Measurement precision
If the system compares files based on multiple properties and dimensions, then the semantic evaluation becomes more comprehensive, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs comprehensive multi-dimensional property extraction and semantic embedding in an offline preprocessing stage, storing the results as property vectors in a database. This preliminary computation of complex semantic features eliminates the need for real-time complex processing during recommendation queries, reducing online system complexity while maintaining comprehensive semantic evaluation.
Solution Approach 2:
The patent creates simplified vector representations (copies) of the complex multi-dimensional music file properties that capture the essential semantic information. These property vectors serve as compact proxies for the full multi-dimensional property space, enabling efficient comparison and search while preserving semantic meaning.
Data Source
AI summary
The invention provides for the evaluation of semantic closeness of a source data file relative to candidate data files. The system includes an artificial neural network and processing intelligence that derives a property vector from extractable measurable properties of a data file. The property vector is mapped to related semantic properties for that same data file and such that, during ANN training, pairwise similarity/dissimilarity in property is mapped, during towards corresponding pairwise semantic similarity/dissimilarity in semantic space to preserve semantic relationships. Based on comparisons between generated property vectors in continuous multi-dimensional property space, the system and method assess, rank, and then recommend and/or filter semantically close or semantically disparate candidate files from a query from a user that includes the data file. Applications of the categorization and recommendation system apply to search tools, including identification of illicit materials or logically progressive associations between disparate files.