Content Recommendation Models Using Gray-Code Attribute Encoding
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Solution Overview
Problem
Existing content recommendation systems fail to accurately predict user interactions with content items due to their inability to account for the continuity and grouping of attribute values, such as time and age, which are continuous in the real world but represented as discrete values, leading to reduced precision and accuracy in recommendations.
Innovation Solution
Encoding continuity and grouping information using gray codes into a data structure to train a model, allowing it to consider multiple options for attribute values, thereby improving the model's ability to predict user interactions with content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete finite attribute values are used in machine learning models, then the model can be trained effectively with limited data, but the precision and accuracy of predictions are reduced because continuity information is lost
Solution Approach 1:
The patent applies dimensionality change by transforming discrete attribute values into multiple binary dimensions using gray code representation. Each discrete value is encoded across several binary fields, where the combination of binary values represents the original discrete value. This allows the model to capture continuity information by learning relationships across multiple binary dimensions rather than treating each discrete value as an isolated category.
Solution Approach 2:
The patent changes the parameter representation from discrete categorical values to binary encoded values. By transforming the attribute values into a binary format (0s and 1s) that preserves continuity information through the encoding scheme, the model can process continuous-like information while maintaining compatibility with standard machine learning algorithms that work with discrete inputs.
2Productivity
If individual attribute hours are considered in isolation, then the model can process each attribute independently, but the ability to account for similar user behavior at nearby times is lost
Solution Approach 1:
The patent merges multiple discrete attribute values into a unified binary encoding representation. By combining the information from multiple discrete hours into a continuous binary space, the model can identify patterns and similarities across nearby times. The gray code encoding ensures that adjacent values have similar binary representations, allowing the model to capture temporal continuity while maintaining processing efficiency.
3Device complexity
If discrete categorical encoding is used for attributes, then the model structure remains simple, but the precision of capturing continuous attribute relationships deteriorates
Solution Approach 1:
The patent segments each discrete attribute value into multiple binary components through gray code encoding. Instead of treating each discrete value as a single unit, the encoding breaks it down into several binary digits that collectively represent the original value. This segmentation allows the model to process each binary component separately while reconstructing the continuous relationship through their combination, thereby maintaining model simplicity while improving precision.
Data Source
AI summary
One or more computing devices, systems, and/or methods for content recommendation based upon continuity and grouping information of attributes are provided herein. User interaction data specifying whether users interacted with content items, user attributes of the users, and content attributes of the content items is obtained. A data structure is populated with the user interaction data. The data structure is modified by inserting a set of sub-fields into the data structure for a user attribute. A sub-field is populated with a value representing an option of the user attribute. The set of sub-fields are an encoding of continuity information and grouping information representing options for the user attribute. The data structure is processed using machine learning functionality to generate a model. The model is utilized to generate a prediction as to whether a user will interact with a content item.