Category-Specific Ranking Models for Search Result Relevance
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Solution Overview
Problem
Existing search engines face challenges in accurately ranking multi-category search results, often prioritizing documents from more frequently selected categories over others, leading to less relevant results being presented first, especially when user intent is unclear or when results span multiple product or service categories.
Innovation Solution
The development and application of category-specific ranking models that segregate search results by relevance and rank before sorting, ensuring that each category is adequately represented in the initial set of search results, and using multi-modal vector models to better match user queries with document features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single unified ranking model is used for all search results, then the ranking process is simple and fast, but the relevance accuracy for different categories deteriorates
Solution Approach 1:
The patent divides the search results into multiple categories and applies separate ranking models to each category. This segmentation allows each model to be optimized for its specific category, improving ranking accuracy without requiring a single overly complex universal model. The system creates category-specific ordered sub-lists that are then combined into a final ranked list.
2Reliability
If documents are ranked by overall relevance across all categories, then the ranking process is straightforward, but documents from less frequent categories are pushed to lower positions, reducing their visibility
Solution Approach 1:
By segmenting results by category and ranking each separately, the system ensures that documents from all categories are fairly represented in their respective ordered sub-lists. This prevents domination by frequent categories while maintaining overall search effectiveness.
Solution Approach 2:
The patent introduces a new dimension to ranking by creating category-specific ordered sub-lists before combining them. This multi-dimensional approach allows the system to consider both category-specific relevance and overall search intent, balancing fairness and effectiveness.
3Measurement precision
If category-specific ranking models are applied to each category, then the relevance accuracy for each category improves, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary categorization of search results before applying category-specific ranking models. This preliminary action organizes the data structure in advance, allowing efficient application of multiple ranking models without excessive computational overhead during the actual ranking process.
4Reliability
If search results are returned without category-based segmentation, then the result set is compact and easy to process, but the initial results may contain noise from irrelevant categories
Solution Approach 1:
The patent segments search results by category and applies filtering and ranking to each segment separately. This removes noise from irrelevant categories while maintaining the compactness of each category-specific result set. The category identifiers act as filters that eliminate irrelevant results early in the process.
Data Source
AI summary
Methods of operating a search engine may include calculating multi-modal document vector models for each of a plurality of electronic documents, training category-specific, search query-specific ranking models with respective machine learning algorithms based on those document vector models, and applying each of those models to further instances of the same search query to rank the documents responsive to that search query.