Search Category Prediction for Query Accuracy
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Solution Overview
Problem
Users often face inaccuracies in search results due to mismatched search queries, leading to wasted time or abandonment of search providers when seeking specific items online, as queries may not accurately reflect intended search results.
Innovation Solution
A system that predicts browse node or category associated with a search query, even if the category is not a keyword, to enhance user experience by adjusting the user interface and search results based on predicted probabilities of user actions, using methods like simple prediction, smoothed browse node prediction, expanded query group, N-Gram generative model, and interpolation between multiple prediction models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search queries are used to find items online, then users can access search results, but the search queries may not accurately reflect what the user wants to see, leading to irrelevant results
Solution Approach 1:
The patent introduces category predictions as an intermediary layer between the search query and search results. The system predicts one or more categories that the user is likely interested in based on the search query, then uses these predictions to adjust and refine the search results. This intermediary category prediction mechanism bridges the gap between ambiguous user queries and relevant results, resolving the contradiction between maintaining simple query input and achieving accurate results.
2Measurement precision
If users spend time altering and adjusting search queries to get relevant results, then they may find what they want, but they waste a significant amount of time
Solution Approach 1:
The system performs preliminary action by automatically predicting categories and adjusting search results before the user has a chance to manually refine their query. The category prediction mechanism proactively analyzes the search query, determines relevant categories, and modifies the search results accordingly, eliminating the need for users to spend time iteratively adjusting their queries to achieve relevant results.
3Ease of operation
If the system provides generic search results without category adjustment, then the search process is simple, but users may give up using a particular search provider
Solution Approach 1:
The system implements self-service by automatically performing category prediction and result adjustment without requiring user intervention. The search provider autonomously analyzes queries, predicts categories, and refines results, maintaining the simplicity of the search process for users while significantly improving result relevance and user satisfaction through automated intelligent processing.
Data Source
AI summary
Techniques for providing category predictions may be provided. For example, a process may attempt to improve a user experience when the user provides a search query. The process can predict the category associated with the search query, even when the category is not a keyword in the search query. Once the category is determined, data may be provided for the particular category, including data that enables an adjustment of a user experience. For example, when the category is apparel, the user experience may include an image-heavy layout and, when the category is books, the user experience may provide more text.