Personalized Navigation for Search Engine Re-visitation Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Search engines face challenges in accurately predicting user intent for repeated queries, as the same keywords can refer to different sites or information objects for different users, leading to suboptimal search results for re-visitation scenarios.
Innovation Solution
The implementation of personalized navigation techniques that analyze query logs and user behavior to identify personal navigational queries (PNQs), allowing for personalized search results by predicting future navigational behavior based on past re-visiting patterns and associating queries with specific sites or information objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines use traditional search algorithms to handle repeated queries, then the search system remains simple and easy to operate, but the search result relevance deteriorates because the same keywords can refer to different sites for different users
Solution Approach 1:
The system performs preliminary analysis of query logs and user behavior patterns to identify personal navigational queries before processing the actual search. By pre-identifying PNQs through pattern recognition in historical data, the system can apply personalized navigation rules in advance, improving result relevance without adding complexity during the main search processing
Solution Approach 2:
The search processing is segmented into different handling paths: traditional search algorithms for standard queries and personalized navigation for identified PNQs. This segmentation allows the system to maintain simplicity for the majority of queries while applying enhanced personalization only where needed, balancing precision improvement with system complexity management
2Measurement precision
If the search engine analyzes query logs and user behavior to identify personal navigational queries, then search result relevance is improved for re-visitation queries, but processing time increases
Solution Approach 1:
Query log analysis and user behavior pattern identification are performed as preliminary actions during system operation, building up personalized navigation models in advance. This allows the actual search processing to benefit from pre-computed patterns without performing time-consuming analysis during each query, thus improving relevance while minimizing processing time delay
Solution Approach 2:
The system replaces complex real-time analysis mechanisms with pre-computed personalized navigation models and pattern recognition rules. By substituting heavy mechanical processing with more efficient pattern-matching algorithms against pre-established user behavior models, the system achieves high relevance with reduced processing time
3Speed
If the search engine personalizes results based on past re-visiting patterns, then navigation speed is improved, but the system requires more sophisticated algorithms increasing device complexity
Solution Approach 1:
The system uses self-service pattern recognition where the search engine automatically identifies personal navigational queries by analyzing its own query logs and user behavior patterns. This self-identification mechanism eliminates the need for complex external intervention or sophisticated real-time analysis, achieving fast personalized navigation through autonomous pattern recognition
Solution Approach 2:
The system changes the parameter of query handling by introducing a new classification dimension: standard queries versus personal navigational queries. By parameterizing the search process to handle different query types differently, the system achieves enhanced navigation speed for PNQs without requiring uniformly complex algorithms across all query types
Data Source
AI summary
Personalized navigation for one or more individuals' use of a search engine is provided. Identification of a query submitted to the search engine is performed. If the query is identified to be a personal navigational query, which is a query via which the individuals intend to navigate to a particular site or information object that they have previously viewed, the particular site or information object associated with the query is identified, and results of the search are personalized based on knowledge of the identified site or information object.