Search Result Ranking Using Segmented Win-Loss Prediction
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Solution Overview
Problem
Existing search engine ranking algorithms are ineffective in determining the optimal position for vertical search results when click-through rates (CTR) of neighboring results are similar, leading to suboptimal user search intent satisfaction, increased computing resource usage, and unnecessary strain on battery-powered devices.
Innovation Solution
Implementing a dedicated machine learning algorithm to predict the win and loss components of the usefulness parameter for each search result, allowing for independent calculation and adjustment of positions to maximize user interest and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engine ranking algorithms are used to determine positions of search results, then the general ranking function is maintained, but the optimal position for vertical search results cannot be determined when CTRs of neighboring results are similar, leading to suboptimal user search intent satisfaction
Solution Approach 1:
The patent segments the usefulness parameter calculation into two independent components: a win component (predicting user interest in a given search result) and a loss component (predicting user interest in the following search result). These components are calculated using separate machine learning algorithms, allowing independent optimization and more precise position determination for vertical search results.
Solution Approach 2:
The patent introduces a machine learning-based prediction model as an intermediary between the CTR data and the final ranking decision. This intermediary processes the win and loss components through trained algorithms to determine the optimal position, enabling more accurate position determination when CTRs are similar.
2Reliability
If multiple search requests are submitted to clarify search intent, then user information needs can be better satisfied, but computing resource usage and battery strain increase
Solution Approach 1:
The patent performs preliminary action by using machine learning models to predict user interest and determine the optimal position of vertical search results before the user submits additional search requests. By accurately ranking results in the initial SERP based on predicted usefulness, the system reduces the need for users to submit multiple clarifying search queries, thereby saving computing resources and battery energy.
3Measurement precision
If machine learning algorithms are used to predict win and loss components independently, then the placement of search results can be optimized to align with user intent, but the device complexity increases
Solution Approach 1:
The patent divides the complex ranking problem into segmented, manageable components: the win component algorithm and the loss component algorithm. Each component is trained independently on specific aspects of user behavior data, reducing the overall complexity compared to a single monolithic ranking algorithm while maintaining high precision in result placement.
4Device complexity
If existing ranking methods are used, then device complexity is lower, but resource usage increases due to multiple search requests
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning models are trained on historical CTR data and user interaction patterns. The win and loss components provide feedback about user interest, allowing the system to continuously optimize search result placement. This feedback loop improves search efficiency by reducing the need for multiple user requests, as the initial ranking is already optimized based on learned patterns.
Data Source
AI summary
There are discloses methods and systems for generating a search engine results page (SERP). The method is executable at a server executing a search engine, the server being accessible via a communication network by at least one electronic device. The method comprises, as part of generating a search result list, the search result list containing a first search result and a second search result, predicting a first interest parameter for the first search result; predicting a second interest parameter for the second search result; predicting a usefulness parameter for the first search result, the predicting being at least partially based on the first interest parameter and the second interest parameter; adjusting a position of the first search result within the ranked search result list based on the predicted usefulness parameter, the adjusting resulting in the first search result being at an adjusted position within the ranked search result list.