Distance-Aware Embedding for Location Feature Representation
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Solution Overview
Problem
Conventional methods for predicting user location, such as those using one hot vectors for zip codes, face challenges with high dimensionality and inability to capture geographical relationships, leading to inefficient model training and inference, as well as limitations in representing location features with open vocabularies.
Innovation Solution
The implementation of a distance-aware embedding scheme that learns embeddings based on geographical distances for location features with both fixed and open vocabularies, reducing dimensionality and enabling the representation of relative distances between locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If one hot vector representation is used for location features, then the representation is simple and straightforward, but the dimensionality becomes extremely high and geographical relationships are lost
Solution Approach 1:
The patent transforms the representation parameters from high-dimensional one-hot vectors to low-dimensional dense embeddings. Each location feature is represented by a compact vector of fixed dimension (e.g., 16 dimensions) instead of thousands of dimensions, fundamentally changing the parameter structure to achieve both simplicity and efficiency.
Solution Approach 2:
The patent introduces a new dimensional space for location representation. Instead of using the original high-dimensional one-hot space, it projects location features into a lower-dimensional embedding space where geographical relationships can be captured, effectively changing the dimensional structure of the representation.
2Ease of manufacture
If one hot vector representation is used for location features, then the representation method is simple, but the ability to capture geographical relationships is lost
Solution Approach 1:
The patent changes the representation parameters to include distance-aware embeddings that explicitly encode geographical relationships. The embedding vectors are trained to preserve spatial relationships, allowing the model to capture proximity and geographical patterns while maintaining representation simplicity.
Solution Approach 2:
The patent introduces distance matrices as an intermediary structure that captures geographical relationships between locations. This distance information serves as a mediator between the embedding representations and the actual geographical spatial relationships, enabling the model to infer proximity and spatial patterns.
3Adaptability or versatility
If conventional deep learning models are used for location prediction, then the model can process various features, but the training efficiency and inference speed are limited due to high dimensionality
Solution Approach 1:
The patent fundamentally changes the dimensional parameters of location features from thousands of dimensions to tens of dimensions. This parameter reduction dramatically decreases the computational complexity of model training and inference while preserving the essential information needed for accurate location prediction.
Solution Approach 2:
The patent performs preliminary embedding learning to transform high-dimensional location features into compact representations before feeding them into the main prediction model. This pre-processing step prepares the data in advance, making subsequent training more efficient and faster without losing important feature information.
4Quantity of substance
If one hot vector representation is used, then fixed vocabulary locations can be represented, but open vocabulary locations cannot be effectively represented
Solution Approach 1:
The patent creates a universal embedding representation that can handle both fixed and open vocabulary locations. The same embedding mechanism and distance-aware training approach work for known locations in the training set and new locations encountered during inference, making the system versatile across different vocabulary types.
Solution Approach 2:
The patent changes from discrete one-hot encoding (which requires predefined categories) to continuous embedding vectors (which can represent any location). This parameter transformation enables the system to generalize to open vocabulary locations by learning continuous spatial relationships rather than relying on predefined categorical labels.
Data Source
AI summary
The present teaching relates to method, system, medium, and implementations for characterizing data. A location feature is first received. A distance-aware embedding for the received location feature is obtained, where the distance-aware embedding for the location feature is learned based on distances between different pairs of locations. A representation of the location feature is then generated based on the embedding for location related predictions.