Deep-Learning Embedding Model for Heterogeneous Entity Representation
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Solution Overview
Problem
Current deep-learning models struggle to effectively represent and compare heterogeneous data entities in a meaningful way, limiting their ability to provide accurate recommendations and search results in applications like social networking systems.
Innovation Solution
A deep-learning model is trained to map entities into a multi-dimensional embedding space, incorporating entity attributes, allowing for vector arithmetic operations that enable the identification of similar or complementary entities based on user queries and context changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep-learning models use traditional representation methods for heterogeneous data, then the model structure remains simple, but the ability to meaningfully represent and compare diverse entities is limited
Solution Approach 1:
The patent transforms heterogeneous entity representations from traditional flat structures into multi-dimensional embedding vectors. Each entity is mapped to a point in a continuous vector space where dimensions capture different semantic attributes, enabling meaningful comparison and relationships between diverse entities through geometric operations while maintaining computational efficiency
Solution Approach 2:
The patent changes the parameter representation from discrete categorical values to continuous vector embeddings. By transforming entity attributes into continuous numerical vectors with learned semantic meanings, the model can perform arithmetic operations and capture nuanced relationships between heterogeneous entities, resolving the contradiction between adaptability and complexity
2Measurement precision
If deep-learning models compress heterogeneous data into unified embeddings, then search and recommendation accuracy improves, but the computational complexity of training and processing increases
Solution Approach 1:
The patent performs preliminary action by pre-training embedding models on large corpora to learn meaningful representations of entities and their relationships. This pre-computed embedding space is then reused for multiple search and recommendation tasks, achieving high accuracy while reducing computational complexity during actual query processing
Solution Approach 2:
The patent creates simplified copies of complex heterogeneous data in the form of compact embedding vectors. These vector representations preserve essential semantic information while being computationally efficient to store and process, enabling accurate search and recommendation without handling the full complexity of original heterogeneous data structures
3Reliability
If entity embeddings incorporate multiple attributes, then the quality of recommendations improves, but the dimensionality of the embedding space increases
Solution Approach 1:
The patent merges multiple entity attributes into a unified embedding vector that captures diverse information in a single coherent representation. By combining demographic, behavioral, and contextual attributes into one integrated vector per entity, the model achieves high recommendation quality while avoiding the computational burden of handling separate high-dimensional attribute spaces
Solution Approach 2:
The patent creates universal embedding vectors that serve multiple functions simultaneously - representing entity identity, capturing attribute information, enabling similarity computation, and supporting arithmetic operations. This multi-functionality allows the same embedding structure to handle diverse attributes without proportionally increasing dimensionality, maintaining recommendation quality while controlling computational complexity
Data Source
AI summary
In one embodiment, a method involves accessing training data, where the training data contains an ordered sequence of data associated with a plurality of entities, training one or more deep learning models to determine, from the ordered sequence of data, a first set of embeddings for each entity of the plurality of entities, where each entity has a plurality of entity attributes, determining, for each of the plurality of entity attributes, a corresponding initial embedding, training the one or more deep-learning models to refine the initial embeddings according to one or more criterion, generating one or more updated embeddings for each of the plurality of entities based on the refined initial embeddings of the plurality of entity attributes, and modifying the first set of embeddings based on the one or more updated embeddings.