Search Ranking Model Using Precomputed Query-Result Scores
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Solution Overview
Problem
Existing information retrieval systems face challenges in optimizing search results relevancy due to general queries, misspelled words, and changing user needs over time, which require an adaptive approach to maintain contextual relevance.
Innovation Solution
A method and system that track user interactions across multiple search sessions to associate search queries with accessed results, calculate result scores, and generate a ranking model to improve the ranking of subsequent search results, incorporating user feedback and contextual changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search ranking methods are used, then the system is simple to operate, but the search result relevancy deteriorates when queries are general, contain misspelled words, or user needs evolve over time
Solution Approach 1:
The system pre-calculates and stores result scores for query elements paired with accessed search results during tracking phase. This preliminary computation enables faster, more accurate ranking when queries are executed, resolving the contradiction by preparing relevance data in advance rather than computing it in real-time during search operations.
Solution Approach 2:
The system tracks user interactions with search results and uses this feedback to continuously update and refine the ranking model. By incorporating actual user behavior data (which search results are accessed for which queries), the system adapts to evolving user needs and improves relevancy over time while maintaining a structured approach to complexity.
2Adaptability or versatility
If the system tracks and analyzes user interactions across multiple search sessions, then the adaptability to evolving user needs improves, but the data processing complexity increases
Solution Approach 1:
The system extracts only the essential elements needed for ranking - specifically, the associations between query elements and accessed search results - from the complex stream of user interaction data. By focusing on this extracted subset of information rather than processing all raw interaction data, the system achieves adaptability while controlling processing complexity.
Solution Approach 2:
The system performs preliminary tracking and association of query elements with accessed search results during user interactions. This pre-processing organizes the data in a structured format that can be efficiently reused for ranking multiple queries, reducing the complexity of real-time data processing while maintaining high adaptability.
3Measurement precision
If the system calculates result scores for query element pairings, then the measurement precision of search result relevance improves, but the computational time increases
Solution Approach 1:
The system pre-calculates result scores for query element pairings during the tracking phase and stores them in the ranking model. When queries are executed, these pre-computed scores are retrieved and applied directly, eliminating the need for time-consuming real-time calculations while maintaining precise relevance measurement.
Solution Approach 2:
The system calculates and stores result scores for a preselection of query elements that are most likely to be relevant, rather than computing scores for all possible query element combinations. This partial computation approach achieves sufficient measurement precision while significantly reducing computational time and resources.
Data Source
AI summary
A method and system for ranking search results is disclosed. Search sessions are tracked to determined accessed search results. For each accessed search result occurring across the plurality of search sessions, the accessed search result is associated with each search query having been executed within a given search session, prior to the user accessing the accessed search result. A preselection is made among the query elements forming each search query, for indexing the selected query elements. For each pairing of a query element from the preselection and an associated accessed search result, a result score is calculated. A ranking model is then generated. The ranking model contains the result score in association with the corresponding pairing, for ranking subsequent search results from subsequent search queries according to the result scores.