Product Suggestion Engine Using Query Occurrence Analysis
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Solution Overview
Problem
Current internet search engines lack the ability to provide precise and complete related product model suggestions based on user queries, failing to facilitate easy comparison and purchase of products with similar attributes.
Innovation Solution
A method and system that receive a product query, determine the number of occurrences of the product in prior queries, identify product categories, and suggest related products with similar attributes, allowing users to select and purchase these products through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a search engine provides basic document search results, then the search functionality is simple and fast, but it cannot provide precise and complete related product model suggestions
Solution Approach 1:
The search system is divided into distinct functional modules: a query processing module that analyzes user queries, a product database module that stores structured product information, and a suggestion generation module that produces related product suggestions. This segmentation allows each module to specialize in specific tasks, improving suggestion precision while managing system complexity through modular design.
Solution Approach 2:
The system pre-processes and structures product information in advance, organizing product attributes, categories, and relationships in a dedicated product database before search queries are submitted. This preliminary organization enables rapid retrieval and precise matching of related products during search operations, enhancing suggestion accuracy without adding complexity to the real-time search process.
2Measurement precision
If the search engine analyzes multiple prior queries and product attributes, then related product suggestions become more accurate, but the processing time increases
Solution Approach 1:
Product attributes, categories, and relationships are pre-analyzed and structured in the product database before queries are processed. This advance preparation enables the system to quickly retrieve and match relevant products based on user queries without performing complex analyses in real-time, thus maintaining high accuracy while minimizing processing time.
Solution Approach 2:
The system automatically analyzes query patterns and product relationships without requiring manual intervention. The query processing module autonomously extracts relevant information from user queries and matches it with pre-organized product data, enabling accurate suggestions to be generated rapidly without human oversight or additional processing delays.
3Adaptability or versatility
If the system provides comprehensive product information including attributes and categories, then users can make better purchase decisions, but the information processing complexity increases
Solution Approach 1:
Product information is segmented into distinct structured categories such as product attributes, specifications, pricing, and category classifications. This organization allows the system to manage comprehensive product data efficiently by processing and retrieving specific information segments based on user needs, reducing overall processing complexity while maintaining information completeness.
Solution Approach 2:
The product database is designed with a universal structure that can accommodate diverse product types and attributes through standardized schemas. This multi-functional design enables the system to handle various product categories and information requirements using the same processing framework, reducing complexity that would arise from separate processing systems for different product types.
4Ease of operation
If the search engine only returns document references, then the system is simple to operate, but it fails to facilitate easy comparison and purchase of products
Solution Approach 1:
The system introduces an intermediary suggestion module that acts as a bridge between basic document search results and comprehensive product information. This module generates and presents related product suggestions with key attributes and comparisons, facilitating easy product evaluation and purchase decisions without requiring users to manually search through multiple documents or navigate complex information structures.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for related search suggestions for products. In one aspect, a method includes receiving a query for searching documents wherein the query specifies a first product. A number of occurrences of the first product in a plurality of prior queries of the documents submitted by users is determined. A product category for the first product is determined based on one or more attributes associated with the first product, the category having a predetermined plurality of category attributes. A suggested product is selected wherein the suggested product is associated with attributes that satisfy the category attributes.