Search Result Ranking Using Machine Learning Models
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Solution Overview
Problem
Existing search result systems often display irrelevant items to users, requiring them to sift through numerous results to find relevant products, leading to a poor customer experience and increased time spent searching.
Innovation Solution
Implementing machine learning processes, specifically using gradient boosted trees models, to determine and rank search results based on relevance and user engagement data, ensuring more relevant items are displayed first, thereby improving user experience and reducing search time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional search algorithms are used to return items matching search terms, then search results are provided to users, but the results include irrelevant items that do not correspond to user intent, requiring users to spend time filtering through irrelevant results
Solution Approach 1:
The system implements feedback loops by collecting user engagement data (clicks, views, conversions) and using machine learning models to continuously refine search result rankings. This feedback mechanism allows the system to learn from user behavior and improve relevance over time, reducing the time users spend filtering irrelevant results
Solution Approach 2:
The patent transforms the search ranking problem by changing from simple keyword matching to a multi-parameter evaluation system using machine learning models. The system considers multiple features including user engagement metrics, item attributes, and contextual signals to dynamically adjust ranking parameters, thereby improving search efficiency and reducing time spent on irrelevant results
2Adaptability or versatility
If search results are ranked by traditional algorithms, then items are displayed in a standardized order, but the results do not account for individual user interests or engagement patterns, requiring users to peruse through many items to find what they are interested in
Solution Approach 1:
The system applies local quality by customizing search results for each individual user based on their unique engagement patterns and preferences. Instead of a uniform ranking approach, the machine learning models generate personalized rankings that adapt to each user's specific interests and behavior, making the search results locally optimized for each user
Solution Approach 2:
The system performs preliminary action by pre-processing and analyzing user engagement data to build personalized user profiles before search queries are submitted. This advance preparation allows the machine learning models to quickly generate personalized rankings when users submit search queries, reducing the time needed to locate items of interest
3Measurement precision
If machine learning models are implemented to determine search result rankings, then more relevant items are displayed first, but the system complexity increases with multiple models and data processing requirements
Solution Approach 1:
The patent applies segmentation by dividing the complex search ranking task into multiple specialized machine learning models, each handling specific aspects of the ranking problem. The system segments the evaluation into different components (user engagement analysis, item relevance assessment, contextual matching) that can be processed independently and then combined, making the overall system more manageable despite the increased complexity
Data Source
AI summary
This application relates to apparatus and methods for ranking search results. In some examples, a computing device receives item data identifying items for a search query provided by a user. The computing determines, for each item, a first value based on a relevance of the item to the search query. The computing device also determines user engagement data for the user, where the user engagement data identifies engagements by the user on a webpage. The computing device determines, for each item, a second value based on the user engagement data. In some examples, the computing device determines the first values and second values by executing one or more machine learning models. The computing device further determines a ranking of the plurality of items based on the first values and the second values. The computing may transmit the ranking of the plurality of items, such as to a web server.