Personalized Search Engine Context Ranking via Semantic Variations
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Solution Overview
Problem
Current search technologies fail to provide efficiently relevant search results due to the lack of 'true relevance' in search results, which is user-specific and context-dependent, leading to inefficiencies in finding electronically stored information within corporate and internet-based searches.
Innovation Solution
The development of personalized search engine methods and systems that determine semantic variations and contexts within user search constraints, using user characteristics and previous search patterns to rank and provide highly relevant search results, continually refining the knowledge base based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines provide search results based on keyword matching and website popularity, then the search system is simple and fast, but the relevance and precision of search results deteriorate
Solution Approach 1:
The search system is segmented into multiple independent modules: user profile analysis module, context analysis module, semantic variation module, and ranking module. Each module processes specific aspects of the search query independently, allowing the system to achieve high relevance through coordinated module outputs without requiring monolithic complexity in a single component.
Solution Approach 2:
User profiles and context information are pre-analyzed and stored before actual search execution. Semantic variations of search terms are pre-computed and cached. This preliminary preparation enables the search system to quickly retrieve and combine pre-processed information during actual searches, achieving high relevance without proportionally increasing real-time computational complexity.
2Measurement precision
If search results are ranked by website popularity, then the search system is easy to implement, but the precision of relevant information retrieval deteriorates
Solution Approach 1:
The ranking algorithm transitions from using单一 parameter (website popularity) to multiple parameters including user profile匹配度, context relevance scores, semantic variation matches, and historical interaction data. Each parameter is weighted and combined to produce a comprehensive relevance score, enabling precise information retrieval through multi-dimensional evaluation rather than simple popularity counting.
Solution Approach 2:
The system incorporates feedback loops where user interactions with search results (clicks, selections, time spent) are continuously monitored and used to refine user profiles and adjust ranking parameters. This feedback mechanism allows the ranking algorithm to adapt and improve precision over time without requiring complete redesign of the ranking system.
3Measurement precision
If comprehensive context analysis and personalization are implemented, then search result relevance improves, but the time required for search processing increases
Solution Approach 1:
User profiles, context information, and semantic variations are pre-analyzed and cached before actual search execution. This preliminary preparation stores processed information in optimized data structures, enabling the search system to quickly retrieve and combine pre-processed data during actual searches without performing complete re-analysis, thus maintaining high relevance while reducing processing time.
Solution Approach 2:
The system implements partial context analysis by focusing on the most relevant user profile attributes and context factors for each specific query type. Rather than analyzing all possible user attributes and context information equally, the system selectively processes only the necessary subset, achieving sufficient personalization and relevance without the full computational overhead of complete analysis.
Data Source
AI summary
The present invention provides search engine methods and systems for generating highly personalized and relevant search results based on the context of a user's search constraint and user characteristics. In an embodiment, upon receipt of a user's search constraint, the method determines all semantic variations for each word within the user search constraint. Additionally, topics can be determined within the user constraint. For each unique word and topic within the user search constraint, possible contexts are determined. A matrix of feasible context scenarios is established. Each context scenario is ranked to determine the most likely context scenario for which the user search constraint relates based on user characteristics. In one embodiment, the weighting used to rank the contexts is based on previous user searches and/or knowledge of their interests. Search results associated with the highest ranking context are provided to the user, along with topics associated with lower ranked contexts.