Hybrid Search Embedding Weighting for Query-Specific Relevance
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Solution Overview
Problem
Hybrid search engines face sub-optimal search outcomes due to fixed or static weights assigned to textual and image-based vector similarity searches, failing to leverage the unique advantages of different embedding models for varying query types.
Innovation Solution
A dynamic weighting module that uses a machine learning model to predict optimal weights for combining search result sets from multiple embedding generators, adjusting weights based on the search query and user interaction, thereby enhancing relevance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple embedding models are used to generate query embeddings for different search operators, then search result relevance is improved, but device complexity increases
Solution Approach 1:
The system divides the embedding generation task into separate modules, with each embedding model (textual, image, video) operating independently to generate embeddings for specific modalities. This segmentation allows each model to specialize in its domain while the overall system integrates results through weighted combination, managing complexity through modular architecture.
Solution Approach 2:
The system employs dynamic weighting where the weights assigned to different embedding models are not fixed but are determined at query time based on the query characteristics and user profile. This dynamic adjustment allows the system to optimize relevance for each specific search scenario while maintaining the ability to handle multiple modalities.
2Ease of operation
If fixed weights are assigned to different embedding models, then system operation is simplified, but search accuracy deteriorates
Solution Approach 1:
The system transitions from static fixed weights to dynamic weights that are computed at query time based on query features and user profile. This dynamic weighting mechanism automatically adjusts the contribution of each embedding model according to the specific search context, improving accuracy without requiring manual configuration or complex user input.
Solution Approach 2:
The system performs self-optimization by automatically determining the optimal weights for different embedding models based on query characteristics and user behavior patterns. The machine learning components continuously learn from user interactions to refine weight assignments, eliminating the need for external tuning while improving search accuracy over time.
3Measurement precision
If dynamic weight prediction using machine learning is implemented, then search relevance is optimized, but computing resources increase
Solution Approach 1:
The system applies partial computation by selecting and weighting only the necessary embedding models based on query characteristics and user profile, rather than always processing all available modalities. This partial action approach reduces computing resources when certain modalities are less relevant, while maintaining high search relevance when multiple modalities provide valuable information.
Solution Approach 2:
The system dynamically changes the weight parameters of different embedding models based on query features and user profile, allowing the same infrastructure to adapt its computational focus. By adjusting weights rather than adding or removing models, the system optimizes resource utilization while maintaining search relevance across different scenarios.
Data Source
AI summary
Methods and systems for optimally weighting search results in a hybrid search framework are described. Responsive to a search query, query embeddings are obtained using corresponding embedding generators. The query embeddings are provided to search operators corresponding to the embedding generators to obtain corresponding search result sets having search results and associated scores. Optimal weights for each of the corresponding embedding generators are determined using a machine learning model, based on the search query. The search result sets are combined, based on the determined weights and the associated scores, yielding a combined search result set. The disclosed methods and systems dynamically optimize weights applied to search result sets that are retrieved using more than one vector-based search operator (e.g., where each search operator performs a vector-based search using embeddings generated by a corresponding embedding generator), for generating more relevant search results within the hybrid search framework.