Predictive Feedback Engine for Tenant-Specific Search Relevance
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Solution Overview
Problem
Current search engines struggle to provide highly personalized and relevant search results due to reliance on term frequency and basic embeddings, failing to account for user preferences and semantic similarity.
Innovation Solution
A predictive feedback engine that utilizes tenant-and user-specific embeddings, leveraging deep learning and neural networks to merge personalization, semantic sense, and predictive analytics, creating a highly configurable and scalable search system with improved relevancy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If term frequency is used to determine search result relevancy, then the search system is simple to implement, but the search relevancy and personalization are sub-optimal
Solution Approach 1:
The patent transforms the search relevancy determination from simple term frequency counting to a complex multi-parameter evaluation system using neural networks. The system processes multiple input parameters including query terms, user profile data, document metadata, and interaction history to generate relevance scores, thereby improving measurement precision through parameter changes.
Solution Approach 2:
The patent introduces neural networks as intermediary components between the raw search inputs and the final relevancy determination. These neural networks act as mediators that process and integrate multiple data sources (query, user profile, document features) to produce refined relevance assessments, resolving the contradiction by adding computational intermediaries.
2Adaptability or versatility
If basic embeddings are used for semantic search, then the implementation is straightforward, but highly personalized results cannot be provided
Solution Approach 1:
The patent segments the embedding system into multiple specialized components: user profile embeddings, query embeddings, document embeddings, and interaction embeddings. Each segment is processed by dedicated neural network layers, allowing personalized results through segmented representation while managing complexity through modular architecture.
Solution Approach 2:
The patent creates composite embedding representations by combining multiple embedding types (user preferences, query intent, document content, interaction history) into a unified relevance assessment. This composite approach enables high personalization by integrating diverse data sources into a cohesive search result ranking system.
3Measurement precision
If deep learning models with multiple hidden layers are trained, then predictive accuracy improves, but training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training neural network models on large corpora before deployment. The tenant-specific and user-specific embeddings are trained in advance using historical interaction data, allowing the system to achieve high predictive accuracy without extensive training time during actual search operations.
Solution Approach 2:
The patent implements local quality by training different neural network components with different levels of complexity based on their specific functions. The embedding layers, attention mechanisms, and prediction layers are optimized independently, allowing high accuracy in critical components while reducing overall training time through localized optimization.
Data Source
AI summary
In some implementations, the techniques described herein relate to a method including: loading, by a processor, a predictive model, the predictive model including a first set of hidden layers; loading, by the processor, a semantic model, the semantic model including a second set of hidden layers; generating, by the processor, a tenant model using the first set of hidden layers and a third set of hidden layers, the third set of hidden layers receiving, as input, an output of the first set of hidden layers; loading, by the processor, a tenanted training data set; training, by the processor, the tenant model by biasing the first set of hidden layers with the second set of hidden layers and training weights of at least the third set of hidden layers using the tenanted training data set; and building, by the processor, an embedding index using the tenant model.