Search Query Suggestion System Using Historical Data Analysis
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Solution Overview
Problem
Search systems, particularly in electronic commerce settings, often fail to surface relevant items when users submit search queries, leading to low conversion rates and unsatisfactory search results due to the lack of matching products in the data store.
Innovation Solution
The system generates suggested search terms by analyzing historical search queries, their metrics, and user behavior, using algorithms to identify similar and relevant queries, and provides these suggestions to users to enhance search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the search system relies solely on keyword matching and relevance algorithms, then the search system remains simple and fast, but it fails to surface relevant items when users submit queries that do not match existing products in the data store
Solution Approach 1:
The system pre-generates suggested search queries based on historical data, product catalogs, and user behavior patterns before users submit their queries. This preliminary preparation of query suggestions allows the system to quickly offer relevant alternatives without complex real-time analysis, resolving the contradiction between improving search relevance and maintaining system simplicity
Solution Approach 2:
The patent introduces an intermediary component that generates suggested queries as a bridge between the user's original query and the product database. This intermediary layer analyzes historical search data and product information to formulate alternative queries, enabling the system to handle cases where direct keyword matching fails without fundamentally complicating the core search mechanism
2Productivity
If the search system provides only exact match results, then the system remains fast and simple, but it yields few and/or no search results when products are not exactly matched in the data store
Solution Approach 1:
The system pre-computes and stores suggested queries during off-peak times based on historical search patterns and product catalog analysis. When users submit queries, the system quickly retrieves pre-generated suggestions rather than performing complex real-time analysis, thus increasing result quantity without significant time loss
Solution Approach 2:
The system dynamically adjusts search parameters by generating alternative query formulations with different keywords, synonyms, and attribute combinations. This parameter transformation allows the system to expand search coverage and find matching products that would be missed by exact matching, while the changes are applied efficiently using pre-computed data
3Ease of operation
If the system does not provide search suggestions, then the system remains simple, but users submit queries that yield low conversion rates and unsatisfactory results
Solution Approach 1:
An intermediary module is introduced that sits between the user interface and the search engine, analyzing user queries and generating suggested alternatives based on historical data and product information. This intermediary handles the complexity of query generation while keeping the core search system simple, improving user effectiveness without overwhelming complexity
Solution Approach 2:
The system implements feedback loops where user interactions with suggested queries are tracked and used to refine future suggestions. Historical search data, click-through rates, and conversion metrics are continuously analyzed to improve the quality of suggested queries, making the system progressively more effective while maintaining manageable complexity through learned patterns
Data Source
AI summary
A user supplied search query is received. Historical search queries are identified that are relevant and/or similar to the user supplied search query. The identified historical queries are then ranked according to various factors. Suggested search queries that are relevant to the user supplied query are identified.