Dynamic Search Relevance via Contextual Theme Generation
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Solution Overview
Problem
Natural language processing models in database search engines often produce biased search results due to semi-fixed filters that lack context, favoring certain content types or lengths over relevance to the user's query.
Innovation Solution
A system that generates a theme for search queries using machine learning algorithms, incorporating user profiles and search histories to identify and score relevant content based on contextual relevance, rather than relying on static filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semi-fixed filters are used in search engines, then search results can be quickly provided, but the results become biased toward certain content types or formats instead of being relevant to the user's query
Solution Approach 1:
The system dynamically adjusts search parameters and filtering criteria based on the specific query and user context rather than applying static, pre-configured filters. The NLP model adapts its behavior in real-time to balance speed and relevance by understanding query intent and adjusting result ranking accordingly.
Solution Approach 2:
The system changes the parameters used for filtering and ranking search results based on the query context. Instead of fixed filters, the NLP model analyzes the query and dynamically modifies search parameters such as relevance weights, content type preferences, and ranking criteria to match the user's information needs.
2Device complexity
If preset filters with minimal context are applied, then search processing is simplified, but contextual relevance of results is reduced
Solution Approach 1:
The system uses feedback from the NLP model's analysis of query context to continuously refine search results. The model evaluates the contextual relevance of each result and adjusts rankings based on this feedback, ensuring that contextual information is preserved and utilized effectively without requiring complex manual filtering.
Solution Approach 2:
The NLP model performs self-service by automatically analyzing query context, identifying relevant information needs, and adjusting search parameters without requiring explicit user configuration or complex filter settings. This maintains simplicity while preserving contextual relevance through intelligent automated analysis.
3Quantity of substance
If natural language processing models are trained without contextual information from search history, then training data requirements are reduced, but accuracy of search results is compromised
Solution Approach 1:
The system performs preliminary analysis of search history and user context before executing the main search query. By pre-processing and understanding the user's search patterns and contextual preferences in advance, the NLP model can achieve higher accuracy with less training data, as the preliminary contextual information serves as a foundation for more efficient learning.
Data Source
AI summary
A system, processes, and a computer-readable storage medium are provided method includes receiving a search query including one or more of a textual phrase, a document, or data including metadata from a user. For example, a processor may execute a theme-generating machine learning algorithm to generate a theme of the search query. Profiles of other users are identified as similar to a profile of the user may be located. A search history of each of the located other users may be located in the collected search history. Content in the searchable content that corresponds to the generated theme of the search query and according to a result of the evaluation of the located search history of each of the other users may be obtained. The obtained content may be scored based on a scoring algorithm and output a set of scored search results to the input/output device.