Entity Scoring Framework Using Multi-Source PlaceRank Signals
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing rating systems for entities of interest, such as restaurants or services, are often unreliable due to biased or misrepresentative user reviews, and lack robustness in evaluating various dimensions of quality and relevance.
Innovation Solution
A system that computes a placerank value for entities of interest by gathering and combining various features from internal and external data sources, including user reviews, API traffic, and social networking data, to provide a comprehensive and reliable score indicative of an entity's importance or relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user reviews are used to generate ratings for entities of interest, then information about entity quality is provided, but the information becomes unreliable due to biased or misrepresentative reviews
Solution Approach 1:
The patent combines multiple data sources including user reviews, expert reviews, entity attributes, and behavioral data into a unified rating system. This merging of diverse information sources compensates for the biases in individual sources, particularly user reviews, to produce more reliable and accurate entity ratings.
Solution Approach 2:
The system introduces an intermediary processing layer that aggregates and weights multiple data sources before generating final ratings. This intermediary mechanism filters out biased information from user reviews by balancing it with objective entity attributes and expert assessments, thereby improving reliability while preserving information accuracy.
2Reliability
If ratings are derived from a small number of sources, then the information is easier to generate, but the information has low statistical significance
Solution Approach 1:
The patent creates a universal rating system that processes multiple types of data sources through a unified framework. The system handles user reviews, expert reviews, entity attributes, and behavioral data using consistent weighting and aggregation mechanisms, achieving high statistical significance while maintaining manageable system complexity through standardized processing.
Solution Approach 2:
The system dynamically adjusts weighting parameters for different data sources based on their reliability and relevance. By changing these parameters adaptively, the system achieves high statistical significance in ratings while keeping the overall system complexity controlled through parameter optimization rather than structural complexity.
3Reliability
If multiple data sources are combined to improve rating reliability, then the scoring becomes more comprehensive, but the processing complexity increases
Solution Approach 1:
The patent segments the rating system into distinct modules: data collection from multiple sources, feature extraction, weighting and aggregation, and final rating generation. This segmentation allows each component to process specific data types independently, improving scoring reliability while managing processing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary processing of data sources before aggregation, including extracting relevant features from reviews, normalizing entity attributes, and pre-computing behavioral metrics. This preliminary action reduces the complexity of the main aggregation process while ensuring comprehensive and reliable scoring through thorough data preparation.
Data Source
AI summary
The present disclosure relates to apparatus, systems, and methods for analyzing characteristics of entities of interest. In particular, the present disclosure provides a mechanism for analyzing information about entities of interest and for rating or scoring the entities of interest based on the analyzed information. The rating or the score of an entity of interest can sometimes be referred to as a placerank value of an entity of interest.