Content Embedding Model for Dynamic Recommendation Accuracy
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Solution Overview
Problem
Existing techniques for determining relevant content to provide to users are not easily adaptable to new content and require significant historical data and processing resources, limiting their effectiveness in recommending content efficiently.
Innovation Solution
A method involving a first machine learning model to generate embeddings of content items, which are then used to train a second model to output vector representations of relevant content items based on user features, allowing for dynamic and flexible content recommendations, including new items by leveraging similarities in embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques use historical user interaction data to determine relevant content, then recommendation accuracy is improved, but processing resources and data requirements increase significantly
Solution Approach 1:
The patent extracts only the essential features needed for recommendation (user features and content embeddings) rather than processing complete historical interaction datasets. The embedding model distills content into compact vector representations, and the recommendation model uses only these extracted features plus current user state, eliminating the need to process vast amounts of raw historical data while maintaining recommendation accuracy.
Solution Approach 2:
The patent transforms the recommendation approach by changing from processing raw historical interaction data to using pre-computed embeddings and current user features. The system transitions from analyzing complete interaction histories to comparing compact vector representations, fundamentally changing the parameter space from high-dimensional raw data to optimized feature vectors, thereby reducing processing complexity.
2Measurement precision
If existing techniques rely on significant historical data to produce accurate recommendations, then recommendation quality is improved, but adaptability to new content deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing embeddings for content items before they are needed for recommendation. The embedding model processes content in advance and stores these representations, so when new content arrives or recommendations are needed, the system can immediately use these pre-prepared embeddings without requiring extensive historical interaction data, enabling rapid adaptation to new content while maintaining recommendation quality.
3Loss of information
If existing techniques analyze large amounts of historical data, then comprehensive user understanding is improved, but processing time and costs increase
Solution Approach 1:
The patent uses copying by creating compact vector representations (embeddings) that capture the essential meaning and characteristics of content items. Instead of storing and processing complete historical interaction records, the system copies the semantic essence of content into embedding vectors, which can be efficiently compared and processed to understand user preferences without the time cost of analyzing full historical datasets.
Data Source
AI summary
Aspects of the present disclosure provide techniques for training a machine learning model. Embodiments include providing features of a plurality of content items as inputs to an embedding model and receiving embeddings of the plurality of content items as outputs from the embedding model. Embodiments include receiving a data set comprising features of a plurality of users associated with content items of the plurality of content items that correspond to the plurality of users. Embodiments include generating a training data set for a machine learning model, wherein the training data set comprises the features of the plurality of users associated with respective labels indicating which respective embeddings of the embeddings correspond to each respective user of the plurality of users. Embodiments include training the machine learning model, using the training data set, to output corresponding embeddings of relevant content items for users based on features of the users.