Search Result Ranking With Multiclass Usefulness Prediction
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Solution Overview
Problem
Search engines face challenges in effectively ranking diverse types of search results, including web and vertical search results, to provide a time-effective SERP that meets user intent, as relevance can vary based on position and user engagement, leading to irrelevant results being overlooked.
Innovation Solution
A multiclassification model is employed to predict the benefit and detriment of ranking positions for search elements, using a blending algorithm to determine a metric value for each element-position pair, considering user engagement probabilities, to optimize the SERP.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional ranking algorithms are used to generate SERP, then processing speed is maintained, but relevance and usefulness of search results deteriorate due to overlooking previously losing elements
Solution Approach 1:
The system performs preliminary classification of search elements into winning and losing categories before final ranking. By pre-identifying elements with potential value (previously losing elements) and preparing them for repositioning, the system can quickly adjust rankings without complete reprocessing, thus improving relevance while maintaining processing efficiency.
Solution Approach 2:
The ranking system dynamically adjusts element positions based on predicted benefit and detriment values. Elements are not statically ranked but can move between positions based on real-time evaluation of their potential contribution to SERP usefulness, allowing the system to adapt rankings without full reprocessing.
2Reliability
If more search elements are considered for ranking, then completeness and relevance improve, but system complexity increases
Solution Approach 1:
The ranking system segments search elements into distinct categories (winning elements, losing elements, repositioning candidates) and applies different evaluation criteria to each segment. This segmentation allows the system to handle complex ranking requirements by breaking down the overall task into manageable sub-tasks, reducing system complexity while maintaining completeness.
Solution Approach 2:
The system introduces intermediate classification layers between raw search results and final ranking. The multiclassification model acts as an intermediary that pre-processes elements and assigns them to categories, simplifying the subsequent ranking process and making the overall system more manageable despite handling diverse element types.
3Ease of operation
If personalized search experience is provided using user interaction data, then user satisfaction improves, but data processing requirements increase
Solution Approach 1:
The system applies personalized treatment locally to specific search elements rather than uniformly processing all results. By identifying which elements benefit most from personalization (repositioning candidates) and applying user-specific adjustments only to those elements, the system improves user satisfaction while minimizing additional data processing energy requirements.
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.