Search Relevance Model Using Precomputed Candidate Ranking
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Solution Overview
Problem
Conventional search approaches in electronic marketplaces are computationally intensive and often fail to provide users with items that are relevant to their queries, leading to suboptimal user experience and reduced profitability for providers.
Innovation Solution
A data-driven system that utilizes historical action data to train language models and generate a product type index, allowing for the identification of items likely to be of interest to users by mapping search queries to product types with associated probabilities, and using these models to rank search results for improved relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language parsing is used on search terms to determine user intent, then the ability to understand user search intent is improved, but computational resources and time required are substantially increased
Solution Approach 1:
The system pre-generates multiple candidate search results for each user query before the user actually views them. This includes pre-computing search results from multiple data sources and pre-determining relevance scores, so that when the user views results, the computationally intensive work has already been completed in advance
Solution Approach 2:
The search system divides the search process into multiple independent segments or threads that operate in parallel. Different candidate search results are generated and evaluated simultaneously through separate computational threads, reducing the overall time and resource requirements by avoiding sequential processing
2Use of energy by moving object
If conventional search approaches are used, then computational resources are consumed, but the relevance of items presented to users deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with search results (clicks, views, purchases) are continuously monitored and used to refine and re-rank candidate search results. This feedback loop ensures that the most relevant items are prioritized based on actual user behavior patterns
Solution Approach 2:
The system dynamically adjusts ranking parameters and relevance criteria based on user profiles, search history, and real-time behavior. By changing the parameters used to evaluate and rank search results based on multiple factors including user preferences and item characteristics, the system improves relevance without requiring proportionally more computational resources
3Productivity
If items more likely to be of interest to users are provided, then user engagement and profitability are improved, but system complexity increases
Solution Approach 1:
The system pre-determines and pre-ranks multiple candidate search results based on predicted user interest before the user actually performs the search. By pre-computing relevance scores and pre-organizing results based on user profiles and historical data, the system reduces the complexity of real-time decision-making while maintaining high relevance
Solution Approach 2:
The system creates and maintains multiple copies or versions of search result sets for different user segments and query types. These pre-computed candidate result sets can be quickly selected and presented based on the specific user and query, avoiding the need to compute everything from scratch and reducing system complexity
Data Source
AI summary
The arrangement and selection of digital content to present to a user can be based at least in part upon probabilities of the user selecting to view more information and/or entering into a transaction with respect to instances of the content. For example, user behavior with respect to various items provided through a content provider can be determined in order to calculate a probability that a user was searching for a particular type of item for a given search. The user behavior can include historical action data, such as information that indicates items that were selected in response to a particular search and/or items that were purchased in response to a particular search. The historical action data can be analyzed to generate an index that indicates a likelihood that the search was intended for a particular type of product. Additionally, the historical data can be used to train language models that can be used to determine a probability of interest for a particular type of product for a given search. Once a query is received, items of interest can be determined using one of the index or language models, and those items can be presented to the user.