Product Placement Engine for Web Page Merchandising
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack the ability to automatically identify relevant products associated with web pages, especially when no products are mentioned, requiring manual effort and increasing costs, and are not effective in performing product specification-based matching and comparison across various web pages.
Innovation Solution
A product placement engine system that parses documents, determines word scores, constructs keyword queries, searches a products database, and assigns scores to identify relevant products, leveraging existing keyword search mechanisms for rapid deployment and minimal administrative input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual product identification and association is performed, then product placement accuracy is improved, but labor costs and time consumption increase
Solution Approach 1:
The system performs automatic product identification and association by analyzing web page content, keywords, and product databases without requiring manual human intervention. The engine autonomously extracts product information, matches it with relevant products in the database, and generates associations, thereby eliminating labor costs and reducing time consumption while maintaining accuracy through automated analysis algorithms.
Solution Approach 2:
The manual mechanical process of product identification and association is replaced with an automated computational system. The product placement engine uses computer-based algorithms to analyze web page content, extract keywords, search product databases, and generate product associations, substituting human manual work with automated information processing and pattern recognition systems.
2Productivity
If automated product identification system is implemented, then labor costs are reduced, but system complexity increases
Solution Approach 1:
The product placement engine is designed as a universal system that can handle multiple functions: analyzing various types of web page content, extracting different kinds of keywords, searching diverse product databases, and generating multiple types of product associations. This multi-functional design consolidates what could be multiple separate complex systems into one unified engine, reducing overall system complexity while maintaining high processing efficiency.
Solution Approach 2:
The system introduces intermediate components such as keyword extraction modules, product database interfaces, and matching algorithms that serve as mediators between the input web page content and the final product associations. These intermediaries break down the complex automated identification process into manageable modular steps, making the overall system more manageable and less complex while achieving high productivity.
3Measurement precision
If product specification-based matching is performed, then product comparison accuracy is improved, but computational requirements increase
Solution Approach 1:
The system extracts only the most relevant product specifications and keywords from web page content and product databases, rather than processing all available information. By selectively extracting key attributes and comparing only those specific parameters, the system achieves accurate product comparison while minimizing computational resource requirements by avoiding unnecessary processing of irrelevant data.
Solution Approach 2:
The system performs partial matching by focusing on key product specifications rather than requiring complete specification comparison. This partial action approach allows the engine to achieve sufficient comparison accuracy for most product matching scenarios without the excessive computational burden of analyzing every single product attribute, thereby balancing accuracy with computational efficiency.
Data Source
AI summary
A product placement engine and method for automatically identifying products for association with a document, the engine including a parser, an analysis module adapted to determine word scores and to adjust the word scores of the words by predetermined weightings, a keyword constructor module adapted to construct a keyword query search string using words having the highest word scores, a search engine adapted to search a products database having product records to identify products satisfying the keyword query search string and assign product scores, and a post processing module adapted to identify word matches in each of the product records and the document and update the product score.