User Profile Embeddings for Niche Content Search Ranking
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Solution Overview
Problem
Existing information search and retrieval systems fail to provide targeted and timely content that is specific to user interests, often missing niche content that is not indexed in consolidated sources and not tailored to individual user profiles.
Innovation Solution
A targeted search system uses user embeddings derived from profiles to identify semantically similar document embeddings, reducing the corpus for processing and employing transformer models to rank documents based on relevance, novelty, serendipity, diversity, and explainability, thereby recommending content that aligns with user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword-based search systems are used to retrieve indexed content, then results match user provided keywords, but the content is not tailored to individual user interests and misses niche content
Solution Approach 1:
The system transforms user profiles and document content into embedding vectors in a semantic space, changing the parameter representation from keywords to continuous vector coordinates. This enables precise measurement of semantic similarity between user interests and document content through distance calculations in the embedding space, resolving the contradiction by making both keyword matching and user personalization operable in the same vector space
Solution Approach 2:
Document embeddings serve as an intermediary representation that bridges user profiles and content repositories. The embedding layer translates both user interests and document content into a common semantic space, enabling the system to retrieve niche content that matches user interests without being limited to pre-indexed consolidated sources
2Measurement precision
If a large corpus of document embeddings is processed to find relevant content, then comprehensive coverage is achieved, but computational cycles and memory consumption increase significantly
Solution Approach 1:
The system segments the document corpus into clusters based on embedding similarity, then uses approximate nearest neighbor search to efficiently locate relevant clusters before performing detailed similarity calculations. This hierarchical segmentation reduces the search space from the entire corpus to only relevant document subsets, maintaining retrieval accuracy while improving processing speed
Solution Approach 2:
The system pre-computes and stores document embeddings in an approximate nearest neighbor index structure before retrieval operations. This preliminary action transforms the expensive real-time similarity computation into a faster approximate search operation, enabling the system to process large corpora efficiently without sacrificing too much retrieval precision
3Measurement precision
If transformer models are used to rank documents based on multiple factors, then high quality targeted results are generated, but computational complexity increases
Solution Approach 1:
The system applies different ranking criteria to different document types and positions in the result set. Transformer models compute attention weights that dynamically adjust the importance of various ranking factors (relevance, novelty, serendipity, diversity, explainability) based on the specific document-user context, enabling high precision ranking without uniformly applying complex models to all documents
Data Source
AI summary
Aspects of the present disclosure relate to systems and methods for performing targeted searching based on a user profile. In examples, a user profile including a user embedding may be retrieved based on the receipt of a user indication. The user embedding may be created based on one or more user interest. A plurality of document embeddings may be identified based on the user embedding, where each document embedding of the plurality of document embeddings is determined to be within a first distance of the user embedding. In examples, a ranking for each document embedding of the plurality of document embeddings may be generated, where the ranking for each document embedding of the plurality of document embeddings is based on the user embedding. At least one document may be recommend based on a ranking associated with a document embedding.