Language Model Kernel Vector Space for Adaptive Document Retrieval
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Solution Overview
Problem
Traditional information retrieval systems face challenges in dynamically determining an optimal vector space for document representation and incorporating user feedback, particularly in utilizing both positive and negative feedback to improve retrieval results.
Innovation Solution
The system converts user queries and documents into language models, uses a kernel function to map these models into a vector space, and applies a double learning strategy with machine learning algorithms to refine the query model and generate a decision boundary based on user feedback, enabling adaptive learning and improved relevance ranking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional vector space model is used for document representation, then documents can be represented as vectors in a fixed space, but the model cannot dynamically adapt to different user information needs and lacks systematic method for constructing vector space
Solution Approach 1:
The patent applies dynamics by making the vector space construction adaptive to different user information needs. Instead of using a fixed vector space, the system dynamically determines the vector space structure based on the query and user feedback, allowing the same system to handle diverse retrieval tasks effectively.
Solution Approach 2:
The patent incorporates feedback mechanisms where user relevance feedback is used to refine the query model and adjust the vector space representation. The system learns from user interactions to improve future retrieval results, creating a feedback loop that enhances adaptability.
2Measurement precision
If language modeling approach is used to represent documents, then systematic representation based on term distributions is achieved, but the approach does not provide explicit model for relevance and difficulty incorporates user feedback
Solution Approach 1:
The patent merges the language modeling approach with user feedback mechanisms. It combines the systematic term distribution representation from language modeling with relevance feedback from users, creating an integrated model that maintains precision while gaining adaptability to user needs.
Solution Approach 2:
The system incorporates user feedback to refine the query model and adjust document representations. Relevance feedback from users is used to learn optimal decision boundaries and improve the relevance scoring, enabling the system to adapt to individual user information needs while maintaining systematic representation.
3Reliability
If machine learning algorithms like SVM are applied to determine decision boundaries, then optimal separation between relevant and non-relevant documents is achieved, but the process requires training data and computational resources
Solution Approach 1:
The system uses user feedback to train machine learning algorithms, creating a feedback loop where retrieval results are evaluated and used to refine the decision boundaries. This feedback mechanism improves reliability by continuously optimizing the separation between relevant and non-relevant documents based on actual user needs.
Solution Approach 2:
The patent changes parameters such as the decision boundary coefficients and weighting factors based on training data and user feedback. By adjusting these parameters dynamically, the system optimizes the separation between relevant and non-relevant documents while managing computational resources through efficient parameter updates.
Data Source
AI summary
Various embodiments provide a system, method, and computer program product for sorting and/or selectively retrieving a plurality of documents in response to a user query. More particularly, embodiments are provided that convert each document into a corresponding document language model and convert the user query into a corresponding query language model. The language models are used to define a vector space having dimensions corresponding to terms in the documents and in the user query. The language models are mapped in the vector space. Each of the documents is then ranked, wherein the ranking is based at least in part on a position of the mapped language models in the vector space, so as to determine a relative relevance of each of the plurality of documents to the user query.