Search Engine Essential Pages Module SFFS Algorithm
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Solution Overview
Problem
Current search engines inefficiency in handling content redundancy, where they focus on individual page relevance rather than the collective coverage of a set of pages for comprehensive query results, leading to unsatisfactory learning experiences for users seeking information on specific subjects.
Innovation Solution
Implementing a search engine with an essential pages module that employs a Sequential Forward Floating Selection (SFFS) algorithm to iteratively select and refine a set of documents that maximize joint query coverage, using a 'bag-of-words' model and word-importance scores to improve search results relevance and diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If search engines return individual page results based on single-page relevance metrics, then the search process is simple and fast, but the results suffer from content redundancy and lack comprehensive query coverage
Solution Approach 1:
The patent merges multiple individual page assessments into a collective evaluation of a set of pages. Instead of evaluating each page independently based on single-page metrics, the system evaluates the joint coverage of a set of pages, combining their information content to reduce redundancy and improve comprehensive query coverage.
Solution Approach 2:
The patent employs dynamic algorithms ( Sequential Forward Floating Selection and Greedy Set Cover) that iteratively optimize the set of selected pages. The system dynamically adjusts the page set by adding or removing pages based on their contribution to overall query coverage, rather than using static single-page ranking metrics.
2Productivity
If search engines evaluate each page individually using traditional metrics, then the processing is computationally efficient, but the results contain redundant content and fail to provide comprehensive subject coverage
Solution Approach 1:
The patent performs preliminary filtering and selection to identify a candidate set of pages before conducting the full collective evaluation. The SFFS and Greedy Set Cover algorithms pre-process the search space by iteratively building and refining the essential pages set, reducing the computational burden of evaluating all possible page combinations.
Solution Approach 2:
The system creates a simplified representation of the search problem using set cover modeling, where pages are represented as sets of covered terms or concepts. This abstract representation allows for efficient computation of collective coverage without requiring detailed analysis of every page's full content.
3Adaptability or versatility
If search engines use simple ranking metrics, then the system is easy to implement and maintain, but the results lack diversity and fail to cover multiple aspects of the query
Solution Approach 1:
The patent implements a universal framework that can handle diverse query types and coverage requirements through the same set cover modeling approach. The system can adapt to different query scenarios by adjusting the coverage criteria and selection parameters, making the algorithm versatile across various search contexts without requiring separate specialized algorithms.
Data Source
AI summary
Systems and methods to perform efficient searching for web content using a search engine are provided. In an illustrative implementation, a computing environment comprises a search engine computing application having an essential pages module operative to execute one or more selected selection algorithms to select content from a cooperating data store. In an illustrative operation, the exemplary search engine executes on a received search query to generate search results. Operatively, the retrieved results can be generated based upon their joint coverage of the submitted search query by deploying a selected sequential forward floating selection (SFFS) algorithm executing on the essential pages module. In the illustrative operation, the SFFS algorithm can operate to iteratively add one and delete one element from the set to improve a coverage score until no further improvement can be attained. The resultant processed search results can be considered essential pages.