Recommendation Algorithm Augments Missing Deal Data Values
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deal analysis systems face challenges in pricing complex service deals due to the difficulty in handling missing values in historical and market data, which hinders accurate cost estimation and probability prediction for service providers.
Innovation Solution
A method and system that utilize a recommendation algorithm to augment missing values in historical or market data for deals, employing a recommendation engine to provide recommended values for missing services, enabling top-down pricing and probability prediction of winning deals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If missing values in historical or market data are left unfilled, then data processing remains simple, but accurate cost estimation and probability prediction cannot be achieved
Solution Approach 1:
A recommendation algorithm is introduced as an intermediary component that automatically fills missing values in the data. The algorithm acts as a mediator between the incomplete data and the analysis system, generating recommended values that enable accurate cost estimation and probability prediction without requiring manual intervention or complex data cleaning processes.
Solution Approach 2:
The system performs self-service by automatically detecting and filling missing values using the recommendation algorithm. Instead of requiring external intervention or complex manual processing, the system autonomously identifies gaps in the data and generates appropriate recommendations, simplifying the overall data processing workflow while maintaining high measurement precision.
2Reliability
If comprehensive service data is collected for all deals, then data completeness improves, but handling missing values becomes more difficult
Solution Approach 1:
The recommendation algorithm operates with feedback mechanisms that continuously refine its recommendations based on the data context. The system analyzes existing service data patterns and uses this feedback to generate more accurate recommended values for missing entries, thereby maintaining high data completeness while reducing the difficulty of missing value handling through iterative improvement.
Solution Approach 2:
The manual or mechanical process of checking and handling missing values is replaced with an automated recommendation algorithm. This substitution transforms the complex manual task of detecting and measuring missing values into an automated computational process that efficiently handles data completeness issues without increasing operational difficulty.
3Manufacturing precision
If manual data completion is used, then data processing remains simple, but pricing accuracy and competitiveness are reduced
Solution Approach 1:
The recommendation algorithm copies and adapts patterns from existing complete service data to generate recommendations for missing values. By analyzing successful pricing patterns from historical and market data, the algorithm creates accurate pricing recommendations that maintain high manufacturing precision while eliminating the need for manual data completion processes.
Solution Approach 2:
The system automatically changes the state of missing data parameters by transforming them from unknown values to recommended values generated by the algorithm. This parameter change enables accurate pricing calculations without requiring manual intervention, thereby maintaining ease of operation while significantly improving pricing accuracy and service provider competitiveness.
Data Source
AI summary
One embodiment provides a method for augmenting missing values in historical or market data for deals. The method comprises receiving information relating to a set of deals. For any service included in one or more deals of the set of deals but not included in one or more other deals of the set of deals, the method further comprises augmenting, for any or all of the one or more other deals that does not include the service, one or more missing values for the service with one or more recommended values based on a recommendation algorithm. The service may be at any service level of a hierarchy of services.