Homogenous User Embeddings from Heterogeneous Interaction Data
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Solution Overview
Problem
Conventional analytics engines struggle to effectively analyze and organize heterogeneous user interaction data from digital content campaigns, leading to inefficiencies and inaccuracies in identifying user segments and behavioral patterns, due to their inability to encode user interactions into uniform representations and manage large volumes of data.
Innovation Solution
The system generates uniform homogenous user embedding representations by transforming user interaction data into structured form using an interaction-to-vector neural network, allowing for semi-supervised training and vectorization of user embeddings for improved analysis and prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional analytics engines are used to analyze user interaction data, then they can process data using existing methods, but they consume significant processing power and fail to efficiently analyze heterogeneous data
Solution Approach 1:
The patent transforms heterogeneous user interaction data into homogeneous vector embeddings, fundamentally changing the data representation parameter. This transformation enables efficient processing by neural networks while reducing computational complexity compared to conventional analytics engines handling raw heterogeneous data
Solution Approach 2:
The patent replaces conventional analytics engine processing with a neural network-based embedding system. The neural network automatically learns patterns from heterogeneous data and produces compact vector representations, substituting the mechanical processing of conventional engines with adaptive neural processing that consumes less power
2Adaptability or versatility
If conventional analytics engines attempt to encode heterogeneous user interaction data into uniform representations, then they can compare users based on interactions, but they struggle to efficiently organize and analyze the data
Solution Approach 1:
The patent changes the data representation from heterogeneous interaction records to homogeneous vector embeddings. This parameter transformation enables direct comparison of users through vector similarity operations while the neural network automatically handles the complex organization of heterogeneous data during the embedding process
Solution Approach 2:
The patent introduces vector embeddings as an intermediary representation between raw heterogeneous interaction data and user comparison operations. These embeddings serve as a unified medium that captures user interaction patterns while simplifying subsequent analysis and comparison tasks
3Productivity
If conventional analytics engines analyze user interaction data without uniform representations, then they can process available data, but they produce inaccurate and imprecise results
Solution Approach 1:
The patent transforms interaction data into vector embeddings that capture semantic meaning and patterns in a compressed form. This parameter transformation improves measurement precision by representing user behavior in a continuous vector space where similarities and differences are naturally encoded, enabling more accurate analysis than conventional discrete methods
Solution Approach 2:
The patent maps heterogeneous interaction data into a vector embedding space with predetermined dimensions. This dimensionality transformation allows the system to capture complex user behavior patterns in a structured format that improves analytical precision while maintaining efficient processing throughput
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users.