Machine Learning Ranking for Search Relevance
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Solution Overview
Problem
Current search algorithms on websites often display irrelevant items to search queries, causing users to spend time filtering through non-relevant results, which can lead to lost sales as users may choose to visit competing websites.
Innovation Solution
Training machine learning models using features generated from user session data, item catalog data, and search queries to determine the relevance of items to search queries, ranking items based on query-item relevance scores, and displaying more relevant items first.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional search algorithms are used to display search results, then the system is simple to implement, but the relevance of search results to user queries deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the search query and search results. These models process user queries and item data to generate relevance scores, acting as a mediator that improves result relevance without requiring complete redesign of the entire search system. The models are trained on historical data and integrated into the existing search infrastructure.
Solution Approach 2:
The patent applies preliminary action by pre-training machine learning models on historical search data, user behavior patterns, and item characteristics before deployment. This pre-training establishes baseline relevance scoring capabilities that can be quickly applied to new search queries without real-time complex processing, improving both relevance and efficiency.
2Loss of time
If traditional search algorithms are used, then the system requires minimal processing time, but users spend more time filtering through irrelevant results
Solution Approach 1:
The patent replaces manual user filtering (mechanical process of scrolling and evaluating each result) with an automated machine learning-based relevance scoring system. The model automatically ranks items by predicted relevance, substituting the user's manual filtering effort with intelligent automated sorting that reduces time loss while maintaining high result quality.
3Ease of operation
If more relevant items are ranked higher in search results, then user satisfaction improves, but the complexity of determining relevance increases
Solution Approach 1:
The patent changes parameters by transforming qualitative relevance concepts into quantitative relevance scores through machine learning models. The system processes multiple input parameters (user query features, item attributes, historical data) and outputs a single relevance score parameter that determines ranking position. This parameter transformation simplifies the complexity by providing a clear numerical basis for ranking decisions.
Data Source
AI summary
This application relates to apparatus and methods for training machine learning models, and applying the trained models to determine an item's relevance to a search query. In some examples, features are generated based on item engagement data associated with a search query. First values are generated for each of a plurality of items based on the search query, a first attribute of each of the items, and the item engagement data. Second values are generated for the items based on the search query, the first attributes, a second attribute of each item, and the item engagement data. Third values are generated for the items based on the search query, the first attributes, a third attribute of each item, and the item engagement data. Features are generated based on the first, second, and third values, and a machine learning model is applied to the features to rank items.