Content Item Embeddings for Cold-Start Relevance Prediction
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Solution Overview
Problem
Content providers face challenges in intelligently selecting relevant content items to deliver in response to user requests, as existing systems struggle to accurately model interactions between users and content items, especially for entities with limited online history.
Innovation Solution
A system and method using machine learning techniques to automatically learn latent representations (embeddings) of entity and content item attributes, generating embeddings for users and content items to improve interaction prediction accuracy and reduce required features for faster learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content selection methods are used, then system complexity is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent replaces traditional mechanical content selection rules with a machine learning-based prediction model that uses neural networks to automatically learn patterns from user behavior data, enabling accurate predictions without complex manual curation systems
Solution Approach 2:
The system automatically learns from user interaction data and improves its predictions over time without human intervention, with the model self-adjusting to capture evolving user preferences and content patterns
2Measurement precision
If more features are used to improve prediction accuracy, then measurement precision improves, but learning time increases
Solution Approach 1:
The patent extracts and selects only the most relevant features from available data, using feature selection techniques to identify patterns that drive user behavior while filtering out redundant or less informative features, thereby reducing learning time while maintaining accuracy
Solution Approach 2:
The system dynamically adjusts the number and type of features used based on the amount of available data and computational resources, adapting the model complexity to achieve optimal accuracy-efficiency tradeoff for different scenarios
3Adaptability or versatility
If entities with limited online history are served, then adaptability improves, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies preliminary actions by pre-computing and storing embeddings for content items and users before actual prediction is needed, allowing the system to quickly generate accurate predictions even for entities with limited history by leveraging pre-learned representations
Solution Approach 2:
The system introduces intermediary representations (embeddings) that mediate between raw data and final predictions, creating a compressed latent space that captures essential patterns and enables accurate predictions for cold-start entities by generalizing from similar entities with more data
Data Source
AI summary
Techniques are provided for using machine learning techniques to learn embeddings for content items. In one technique, training data is used to learn embeddings for each attribute value of multiple attribute values of multiple content items, embeddings for each attribute value of multiple attribute values of multiple entities, and weights for a set of contextual features. In response to receiving a content request, a content item that is associated with one or more targeting criteria that are satisfied based on the content request is identified. A first set of embeddings for the content item are identified, a requesting entity that initiated the content request is identified along with a second set of embeddings for the requesting entity, and a set of feature values for the set of contextual features is identified. The content item is selected based on the sets of embeddings, the set of feature values, and the weights.