Embedding-Based Semantic Search for User Content Discovery
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Solution Overview
Problem
The increasing volume of digital content makes it difficult for users to discover relevant content, as existing search techniques, such as keyword searching, are inefficient and do not effectively utilize user behavior or intent.
Innovation Solution
The system represents objects and users as embeddings in a multi-dimensional space, using continuous data to determine relevance based on user behavior and intent, allowing for the presentation of objects of potential interest without relying on explicit keywords or direct relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword searching is used, then search results can be obtained, but the search efficiency and relevance deteriorate due to inability to utilize user behavior and intent
Solution Approach 1:
The patent replaces traditional mechanical keyword-matching search systems with an embedding-based semantic search system. Objects and users are represented as embeddings in a multi-dimensional space, allowing the system to compute similarities based on semantic meaning rather than literal keyword matching, thereby improving both search efficiency and relevance
Solution Approach 2:
The patent transitions from one-dimensional keyword matching to multi-dimensional embedding spaces. By representing objects and users as vectors in a high-dimensional space, the system can capture complex relationships and semantic similarities that are impossible to achieve with traditional keyword searching, enabling more precise relevance measurement
2Adaptability or versatility
If traditional search techniques are used, then simple implementation is maintained, but the ability to discover relevant content deteriorates with increasing digital content volume
Solution Approach 1:
The patent performs preliminary embedding generation for objects and users before actual search operations. By pre-computing and storing embeddings in a database, the system enables fast similarity searches without requiring complex real-time processing, thus improving content discovery capability while managing system complexity through efficient data organization
Solution Approach 2:
The patent introduces embeddings as an intermediary representation layer between raw digital content and search queries. This intermediary embedding space simplifies the search process by transforming complex content into comparable vector representations, enabling the system to handle vast amounts of content more effectively
Data Source
AI summary
The described implementations are operable to determine potential objects of interest to a user based on a blend of the user's long-term behavior and short-term interests. Long term user behavior may be determined for the user over a period of time and represented as continuous data. Short-term interest may be determined based on objects with which the user has recently interacted and attributes of those objects may be represented together as continuous data corresponding to the short-term user interest. The continuous data of the short-term interest and long-term user behavior may be blended to produce a user embedding. The user embedding may then be compared with objects to determine objects that are of potential interest to the user.