Deep Joint Variational Autoencoder for Latent Space Recommendation
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Solution Overview
Problem
Current network interfaces struggle with personalizing user interactions due to the complexity and potential errors introduced by discrete feature selection processes, which are required to extract user preferences from transaction data.
Innovation Solution
A system utilizing a trained deep joint variational autoencoder model to generate latent space representations of user interactions, allowing for the comparison and recommendation of items without the need for explicit feature extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete feature selection or extraction steps are implemented to extract user preferences from transaction data, then user preferences can be identified, but the system introduces unnecessary complexity and potential errors into the customization process
Solution Approach 1:
The patent extracts only the essential latent representations of user preferences and item characteristics from transaction data, eliminating the need for complex discrete feature selection steps. The variational autoencoder model directly extracts meaningful patterns from raw data, taking out only the necessary information while discarding noise and redundancy.
Solution Approach 2:
The patent introduces latent variables as intermediary representations between raw transaction data and final recommendations. These latent variables serve as a mediating layer that captures user preferences and item characteristics in a simplified form, avoiding the complexity of direct feature extraction and selection processes.
2Adaptability or versatility
If discrete feature selection steps are used to personalize user interfaces, then user preferences can be captured, but errors may be introduced into the customization process
Solution Approach 1:
The patent replaces the mechanical process of discrete feature selection with a probabilistic neural network model. Instead of manually selecting and extracting features through multiple discrete steps, the variational autoencoder automatically learns meaningful representations through probabilistic inference, reducing human error and increasing reliability.
Solution Approach 2:
The patent transforms the discrete feature selection process into a continuous probabilistic parameter optimization problem. By changing from discrete feature choices to continuous latent variable optimization, the system achieves more reliable and smooth personalization without the errors associated with discrete decision-making processes.
3Productivity
If traditional recommendation systems suggest categories or items based on prior interactions, then recommendations can be provided, but explicit feature extraction is required which complicates the system
Solution Approach 1:
The patent performs preliminary encoding of user interactions and item characteristics into latent representations during the training phase. This preliminary action creates ready-to-use latent variables that can be quickly compared and matched during recommendation generation, eliminating the need for complex feature extraction at runtime and improving productivity.
Solution Approach 2:
The patent transitions from discrete feature spaces to continuous latent variable spaces, adding a new dimensional perspective to the recommendation process. This dimensionality change allows for more efficient similarity computations and recommendation generation without requiring complex discrete feature extraction steps.
Data Source
AI summary
Systems and methods for generating top-k recommendation using latent space representations generated by deep joint variational autoencoder processes are disclosed. A user identifier is received and a set of prior interactions associated with the user identifier is obtained. A set of latent space representations of the set of prior interactions is generated using a trained inference model. The trained inference model includes a joint variational autoencoder model. A set of k-recommended items is generated based on a comparison of the set of latent space representations of the set of prior interactions and a set of latent space representations of one or more items. A user interface including the set of k-recommended items is generated.