Preference Estimator for Unified SERP Selection
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Solution Overview
Problem
Users face inefficiencies in searching for information due to differences in search engine results pages (SERPs) generated by various search engines for the same query, leading to time-consuming iterations between search engines to find the most preferred results.
Innovation Solution
A computing system with a preference estimator system that identifies and outputs the most preferred SERP from multiple SERPs generated by different search engines, utilizing a classifier to evaluate query features, user profiles, and SERP characteristics to determine user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually switch between multiple search engines to find preferred results, then they can potentially find better search results, but the time consumption increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple SERPs from different search engines and pre-evaluating them using the preference estimator before the user even submits a query. When a query is submitted, the already-prepared and evaluated SERPs can be quickly presented to the user, eliminating the need for real-time switching between search engines while maintaining the ability to provide high-quality results.
Solution Approach 2:
The preference estimator system acts as an intermediary between multiple search engines and the user. It receives SERPs from different search engines, evaluates them according to user preferences and query characteristics, and selects the most appropriate SERP to present to the user. This intermediary layer automates the selection process that would otherwise require manual user intervention, significantly reducing time consumption while maintaining result quality.
2Measurement precision
If users reformulate queries multiple times across different search engines, then they can improve search result relevance, but the complexity of the search process increases
Solution Approach 1:
The system merges the functionality of multiple search engines and their respective SERPs into a single unified interface. Instead of requiring users to navigate between different search engines and manually reformulate queries, the preference estimator system combines multiple SERPs and automatically selects the most relevant one based on query characteristics and user preferences, simplifying the search process while maintaining result relevance.
Solution Approach 2:
The system implements feedback mechanisms where the preference estimator continuously learns from user interactions and SERP evaluations. By analyzing which SERPs users prefer for different query types and characteristics, the system refines its selection process, improving result relevance over time without requiring users to manually reformulate queries or navigate complex search processes.
3Measurement precision
If the system generates and evaluates multiple SERPs using a preference estimator, then the accuracy of selecting the preferred SERP improves, but the computational complexity increases
Solution Approach 1:
The preference estimator system applies partial evaluation by focusing on the most critical features and characteristics of SERPs rather than evaluating every possible attribute. It identifies and prioritizes the key factors that influence user preference for different query types, evaluating only those aspects that have the greatest impact on selection accuracy. This approach maintains high estimation accuracy while reducing unnecessary computational overhead.
Data Source
AI summary
Described herein are technologies pertaining to determining which search engine results page (SERP), from a plurality of SERPs, is preferable to a user. A query is received, and multiple SERPS are retrieved based upon the query, wherein the multiple SERPs are generated independently from one another. Values of features of the query and the multiple SERPs are obtained, and a determination as to which of the SERPs from the multiple SERPs is preferable to the user is made based upon the values of the features. The SERP determined to be preferable over other SERPs in the multiple SERPs is presented to the user.