Category Predictor for Tail Query Product Search
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Solution Overview
Problem
Current database query systems generate inaccurate product listings for tail queries due to the lack of association between uncommon search terms and product categories, leading to slow and irrelevant search results.
Innovation Solution
A category predictor is trained using machine learning techniques to associate uncommon search terms with product categories by analyzing user behavior data and search metrics, combining vector algebra to rank related categories and improve search results for tail queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a conventional search query system is used for tail queries, then the system structure remains simple, but the product listings generated are inaccurate and irrelevant
Solution Approach 1:
The patent introduces a category predictor as an intermediary component between the search query system and the database. This predictor uses machine learning models to infer product categories for uncommon search terms, enabling accurate product listings without requiring direct associations between every possible search term and product category in the database.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on historical search data and user behavior patterns. This allows the category predictor to make accurate predictions for tail queries without requiring real-time complex processing, thus improving accuracy while managing system complexity.
2Measurement precision
If machine learning techniques are implemented to predict product categories, then the accuracy of product listings for tail queries improves, but the computational resources and processing time increase
Solution Approach 1:
The machine learning models are trained in advance on historical search data and user behavior patterns, storing learned associations between search terms and product categories. During actual tail query processing, the system performs lightweight prediction rather than complex real-time analysis, reducing computational resource consumption while maintaining high accuracy.
3Measurement precision
If the database is queried using uncommon terms without category association, then the query process remains fast, but the search results are irrelevant
Solution Approach 1:
The category predictor serves as a mediator that quickly infers product categories for uncommon search terms using pre-trained machine learning models. This allows the system to maintain fast query processing by avoiding complex real-time analysis while still achieving relevant search results through accurate category prediction.
Data Source
AI summary
In various example embodiments, techniques for querying a product in a database of a publication system using a tail query are presented. An application interface module receives, from a device of a user, a tail query for a product in the publication system. The tail query has a common term and an uncommon term. A data storage interface module can access, from a search metrics database, a plurality of product categories associated with the common term, and access user behavior data, with the user behavior data including product views corresponding to past search queries in the publication system. A category predictor can determine a first product category from the plurality of product categories for the tail query based on the user behavior data and information derived from the search metrics database. A listing generator can generate an ordered list of product listings associated with the first product category based on the user behavior data.