E-commerce Search Indexing via External Content Aggregation
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Solution Overview
Problem
Conventional online search systems in e-commerce platforms rely solely on keywords provided by sellers, which may not capture the full range of relevant information, leading to haphazard customer purchases and limited search accuracy.
Innovation Solution
The system supplements existing keywords with external content data from the internet, using machine learning and natural language processing to extract and index additional keywords and phrases from third-party websites, enhancing search results and product categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only seller-provided keywords are used for search indexing, then the search system is simple to maintain, but search accuracy and relevance are limited
Solution Approach 1:
The patent combines multiple keyword sources (seller-provided keywords, external website content, product reviews, and alternative names) into a unified search index. This merging of diverse data sources enriches the keyword set, improving search accuracy and relevance without requiring a complete redesign of the search system.
Solution Approach 2:
The system performs preliminary extraction and processing of keywords from external sources before the actual search operation. By pre-processing and indexing keywords from third-party websites, product reviews, and alternative names in advance, the system prepares enhanced search data that immediately improves search accuracy when queries are executed.
2Measurement precision
If external content data is extracted and indexed to supplement keywords, then search relevance improves, but data processing complexity increases
Solution Approach 1:
The patent extracts relevant keywords and information from external sources such as third-party websites, product reviews, and alternative product names. By selectively extracting only the necessary keyword data from these external sources and integrating it into the existing index, the system enhances search relevance while avoiding the need to process and store all external content.
3Reliability
If comprehensive keyword coverage is achieved through multiple sources, then customer purchase accuracy improves, but information processing time increases
Solution Approach 1:
The system performs preliminary extraction, processing, and indexing of keywords from multiple sources in advance of actual search operations. By pre-processing data from external websites, reviews, and alternative names during off-peak times or in background processes, the system makes comprehensive keyword data immediately available, improving purchase accuracy without adding noticeable delay to customer search operations.
Data Source
AI summary
Systems and methods are disclosed for refining the accuracy of network searches by supplementing existing keywords and key phrases in an e-commerce catalog or other database with aggregated and analyzed additional, external data. The internet or another network can be crawled for identifiers which point to entries in the catalog or other database, and, subject to third-party use restrictions, data and metadata can be extracted to enrich the existing keywords and key phrases. The extracted external content may be processed by machine learning techniques in order to find similar entries in the original catalog or database. Categorizing and indexing the entries further improves search recall, including clustering via processing word embeddings.