Skill-Centric Embeddings for Precise Professional Content Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content distribution systems face challenges in efficiently and accurately matching users' professional skills with relevant digital content, leading to wasted computing resources and user disengagement due to poorly structured embeddings and poor quality embedding-based retrieval.
Innovation Solution
Generating skill-centric user and document embeddings using large language models, optimized through fine-tuning and prompt engineering, to improve matching accuracy and reduce noise in embeddings, thereby enhancing the relevance of content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional embedding methods are used for content distribution, then system complexity is reduced, but matching precision between user skills and content deteriorates
Solution Approach 1:
The patent transforms the embedding representation from traditional dense vectors to skill-centric structured embeddings. This involves changing the parameters of the embedding structure by organizing content and user representations around discrete skill tags and hierarchical skill relationships, thereby improving matching precision while maintaining manageable complexity through structured organization.
Solution Approach 2:
The patent segments the embedding space into skill-specific dimensions and hierarchical skill categories. By dividing the content and user profiles into discrete skill components rather than treating them as monolithic vectors, the system achieves more precise matching while the modular segmented structure actually reduces overall complexity through organized modularity.
2Measurement precision
If skill-centric embeddings are generated using large language models, then embedding quality and matching accuracy improve, but computing resource consumption increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing skill embeddings for content items and user profiles during off-peak times or in advance. This allows the system to generate high-quality skill-centric embeddings using computationally intensive large language models beforehand, then retrieve and match these pre-generated embeddings during runtime with minimal computing resource consumption.
Solution Approach 2:
The patent uses copying by creating and storing skill embedding representations that can be reused across multiple matching operations. Instead of regenerating embeddings for every query, the system creates master skill embedding copies that can be efficiently retrieved and compared, significantly reducing computing resource consumption while maintaining embedding quality.
3Measurement precision
If comprehensive skill analysis is performed to improve matching accuracy, then relevance of content recommendations improves, but processing time increases
Solution Approach 1:
The patent applies dynamics by implementing a multi-level skill matching approach where the system can dynamically adjust the depth of skill analysis based on query requirements. For simple queries, only top-level skill categories are matched, while for complex queries, the system dives deeper into hierarchical skill relationships. This dynamic adjustment maintains high matching accuracy when needed while reducing processing time for routine operations.
Data Source
AI summary
Embodiments extract a key phrase from an online profile of a user of a professional social network (PSN) and identify a set of skills associated with the key phrase. A set of skill embeddings corresponding to the identified set of skills may be retrieved from a store of skill embeddings. The skill embeddings may be pre-created by a large language model (LLM). The retrieved skill embeddings may be aggregated to create a skill-centric digital representation of the user. Based on the skill-centric digital representation of the user, a subset of digital documents may be identified to present to the user via the PSN.