Predictive Conversion System for Search Result Ranking
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Solution Overview
Problem
Traditional search engine technologies fail to accurately recognize the relevance of items to search requests due to incomplete item descriptions and varied terminology, leading to ineffective ranking of search results for commercial transactions.
Innovation Solution
A system and method that predicts sale transaction conversion rates by analyzing item information, extracting relevant metadata, applying logistic regression formulas, and ranking items based on predicted conversion scores, incorporating user preferences for maximizing revenue or user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engine methods are used to rank items based on keyword matching, then the search process is simple and fast, but the relevance recognition accuracy deteriorates due to incomplete item descriptions and varied terminology
Solution Approach 1:
The system performs preliminary actions by discovering and extracting metadata from item information before the search query is processed. This pre-processing of item data including metadata extraction and normalization enables more accurate relevance recognition without adding complexity to the core search algorithm.
Solution Approach 2:
The system introduces metadata as an intermediary layer between the item information and the search query matching process. This metadata acts as a standardized mediator that bridges the gap between varied item descriptions and search terms, improving relevance recognition accuracy without requiring direct complex comparisons of all item attributes.
2Measurement precision
If comprehensive item information is analyzed to improve conversion rate prediction, then the prediction accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the most relevant metadata elements from comprehensive item information using extraction rules specific to each product category. This selective extraction of key metadata attributes rather than processing all available information maintains prediction accuracy while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The system applies different metadata extraction rules and weighting schemes to different product categories, recognizing that different categories require different types of information for accurate conversion rate prediction. This localized approach optimizes processing efficiency by focusing computational resources on category-specific relevant attributes rather than uniformly processing all item information.
Data Source
AI summary
In one embodiment, a system and method of predicting sale transaction conversion rate of an item operates through a search of information in response to a query over a network. The item can be included in a category of items. Information for other relevant items of the category is available through network query and historical data, among others. Respective information for the other items of the category is available. The system and method includes discovering available information of the item of interest, extracting certain of the available information of the item, analyzing the certain information by comparing the information to other item information, weighting the information for the item in comparison to other items of the category, calculating a predictive score for the item of interest, and presenting the information of the item of interest ranked according to the predictive score as compared to other items of the category.