User-driven index selection via machine learning prediction
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Solution Overview
Problem
Existing index generation techniques for web search engines are inefficient due to their reliance on general importance metrics like hyperlink structure, neglecting user search behavior and content, leading to suboptimal selection of web pages for indexing.
Innovation Solution
Implementing a machine learning-based approach that predicts the likelihood of web page searches by analyzing user behavior data from search engine logs, incorporating features such as click-through rates, URL patterns, and content analysis to dynamically rank and select web pages for indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an all-encompassing index of all web pages is created, then the completeness of the index is improved, but the search time and computing resources required increase significantly
Solution Approach 1:
The patent divides the web page population into multiple segments or categories based on user search behavior patterns. Instead of treating all web pages uniformly, the system segments them into groups such as frequently searched, occasionally searched, and rarely searched pages. This segmentation allows the search engine to optimize indexing strategies for each segment, reducing the overall search space and improving search efficiency while maintaining completeness for important segments.
Solution Approach 2:
The patent applies different indexing qualities or depths to different portions of the web based on local user search behavior characteristics. High-value pages that are frequently searched receive more comprehensive indexing with greater detail, while less important pages receive lighter indexing. This local quality differentiation ensures that search resources are concentrated on pages that matter most to users, reducing overall search time while maintaining reliability for critical pages.
2Ease of manufacture
If traditional hyperlink-based importance metrics are used for index selection, then the selection process is simplified, but the accuracy of predicting which pages users will search decreases
Solution Approach 1:
The patent implements a feedback mechanism that uses actual user search behavior data to continuously refine and improve page importance predictions. The system collects feedback from user search queries, clicks, and browsing patterns, then uses this feedback to update the importance scores of web pages. This feedback loop enables the system to progressively improve prediction accuracy by learning from real user behavior rather than relying solely on static hyperlink metrics.
Solution Approach 2:
The patent introduces an intermediary layer between traditional hyperlink metrics and final page selection. This intermediary component analyzes user search behavior patterns and translates them into refined importance scores. Rather than directly using hyperlink metrics or user behavior data alone, the system uses this intermediary analysis layer to combine multiple signals and produce more accurate predictions of which pages users will actually search for.
3Reliability
If a comprehensive index including all web pages is maintained, then no relevant pages are missed, but the index size and storage requirements become inefficient
Solution Approach 1:
The patent extracts and separates out the most important web pages from the general web population based on user search behavior analysis. By identifying and extracting high-value pages that are most likely to be searched, the system creates a focused index subset that contains the essential content users need. This extraction approach reduces index size by eliminating less important pages while maintaining completeness for the extracted high-value subset.
Solution Approach 2:
The patent implements a dynamic index that adapts its composition based on changing user search behavior patterns. Rather than maintaining a static comprehensive index, the system dynamically adjusts which pages are included in the index based on current user behavior data. This dynamic approach allows the index size to optimize itself automatically, including only those pages that are currently relevant to user searches while excluding pages that are no longer frequently searched.
Data Source
AI summary
Techniques for index building are described. Clickcounts of respective training URLs may indicate a number of times that corresponding training URLs were clicked in search engine results. A machine learning algorithm implemented on a computer computes a trained model that is then stored. The clickcounts and respective URLs are passed to the machine learning algorithm to train the model to predict probabilities based on feature vectors of URLs. An index of web pages is built for a set of URLs that identify the web pages. Feature vectors for the URLs are computed. Probabilities of the web pages of the URLs being searched in the future by users may be computed by processing the feature vectors with the trained model. The probabilities may be used to determine which of the URLs to include in the index.