Dynamic E-commerce Search Engine with User Pathing Correction
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Solution Overview
Problem
E-commerce website search engines often fail to accurately locate desired products due to misspellings, unfamiliar industry terminology, and seasonal trends, leading to user frustration and abandonment.
Innovation Solution
A dynamic computer program that analyzes user behavior to correct search errors by calculating confidence scores for search terms, modifying search results to include synonyms and relevant terms, and updating these modifications on a rolling basis to improve search success rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a traditional product search engine is used on an e-commerce website, then users can perform basic product searches, but users often fail to locate desired products due to misspellings, unfamiliar industry terminology, and seasonal trends
Solution Approach 1:
The system implements feedback by tracking user search behavior, analyzing successful search patterns, and using this information to automatically improve search results. The system monitors which search terms lead to successful product locations and uses this feedback to refine future search recommendations and corrections.
Solution Approach 2:
The system performs preliminary action by pre-calculating and storing synonym relationships, related product information, and seasonal trend data before users perform searches. This allows the system to quickly provide corrected search terms and relevant product suggestions without requiring users to manually refine their searches multiple times.
2Reliability
If users perform multiple searches and refine searches to locate desired products, then users may eventually find products, but users become frustrated with cumbersome search processes and abandon the website
Solution Approach 1:
The system pre-processes and stores synonym relationships, related product data, and seasonal trend information in advance. When a user performs a search, the system immediately retrieves pre-computed corrections and suggestions rather than requiring multiple iterative search refinements, significantly reducing the time and frustration associated with product location.
3Adaptability or versatility
If a static product search engine is used, then the search engine structure is simple, but the search engine cannot adapt to seasonal trends and trending products
Solution Approach 1:
The system transitions from a static search engine to a dynamic one by continuously updating search results based on seasonal trends, trending products, and user behavior patterns. The system automatically adjusts search recommendations and product rankings in real-time to reflect current market conditions and user preferences.
Solution Approach 2:
The search engine is enhanced to perform multiple functions beyond basic product matching, including synonym correction, seasonal trend adaptation, related product recommendation, and user behavior analysis. This multi-functional approach allows a single search system to address various user needs and market conditions.
4Reliability
If the search engine includes comprehensive product information and synonyms, then search accuracy improves, but the system requires complex data processing and storage
Solution Approach 1:
The system segments the comprehensive product data into organized categories such as synonyms, related products, seasonal trends, and user behavior patterns. This segmentation allows for efficient storage, retrieval, and processing of large datasets by breaking them into manageable, purpose-specific components that can be independently optimized.
Data Source
AI summary
A system and method for creating an e-commerce, dynamic, internal search engine are disclosed. The system and method include providing a server having a memory and a processor, and providing the server with a search engine configured to perform the steps of: identifying a first user search term that results in a first search result and the number of occurrences the first search term is input into the search engine. The search engine then identifies a second user search term input into the search engine subsequent to the first user search term and the number of occurrences the second search term is input into the search engine and identifies the number of occurrences when the second search term yields a successful search result. The search engine then modifies subsequent search results facilitated by the first user search term to reflect the number of successful search results of the second search term.