Search Result Ranking Using Usefulness Parameter
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Solution Overview
Problem
Current search engine ranking methods do not adequately prioritize search results based on user interaction data, leading to suboptimal presentation of search results to users.
Innovation Solution
A method and system that utilize a usefulness parameter, determined by analyzing user behavior such as click-through rates and time spent on search results, to rank both general and vertical search results, optimizing their positions on the search engine results page (SERP).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search results are ranked using traditional relevance models, then the search engine can locate relevant results, but the results may not reflect actual user preferences and interaction patterns
Solution Approach 1:
The system performs preliminary analysis of user interaction data (clicks, time spent, scroll depth) before generating search results. This advance processing of user behavior data allows the ranking model to incorporate real user preferences into the ranking calculation, improving measurement precision while preserving user preference information.
Solution Approach 2:
The system implements a feedback mechanism where user interaction data from previous searches is continuously fed back into the ranking model. This creates a closed-loop system where the ranking algorithm learns from actual user behavior patterns and adjusts rankings accordingly, ensuring both accurate relevance measurement and preservation of user preferences.
2Measurement precision
If the system analyzes detailed user behavior data to determine usefulness parameters, then ranking accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the user behavior analysis into distinct components: click detection, time spent measurement, scroll depth tracking, and relevance scoring. Each component is processed separately and then integrated into the final ranking calculation. This segmentation reduces system complexity by breaking down the complex analysis task into manageable, independent modules.
Solution Approach 2:
The system implements partial analysis by focusing on the most significant user interaction metrics (clicks and time spent) rather than analyzing every possible user behavior. This selective approach maintains high ranking accuracy while avoiding the complexity of processing excessive data points, applying the principle of doing enough rather than everything.
3Reliability
If search results are ranked based on multiple parameters including usefulness, then result quality improves, but processing time increases
Solution Approach 1:
The system pre-calculates usefulness parameters for search results based on historical user interaction data before the actual search query is processed. This preliminary preparation of ranking data allows the system to quickly retrieve and apply pre-computed usefulness scores during live searches, improving result quality while minimizing processing time for user queries.
Solution Approach 2:
The system applies different weighting parameters to different search results based on their local characteristics and user interaction patterns. Rather than uniformly processing all results with the same complexity, the system adjusts the ranking calculation for each result based on its specific usefulness metrics, improving overall quality while optimizing processing efficiency for each individual result.
Data Source
AI summary
There are disclosed methods and systems for generating a search engine results page (SERP) responsive to receiving a search query. A ranked plurality of search results is generated, including at least one general search result and at least one vertical search result, the ranked plurality of search results having been ranked based at least in part on a usefulness parameter. The usefulness parameter indicates the optimal position of the at least one vertical search result in the ranked plurality of search results based on its determined usefulness relative to the search query. The usefulness parameter is predetermined based on a training set of user data on past user interaction with the at least one vertical search result when its original rank was modified such that the at least one vertical search result was ranked randomly and placed on the previous SERP at a random position.