Multi-Source Entity Scoring for Reliable PlaceRank Evaluation
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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
1Ease of operation
If user reviews are used as the primary information source for entity ratings, then the system is simple to operate and data collection is easy, but the reliability and accuracy of the ratings deteriorate due to biased or misrepresentative reviews
Solution Approach 1:
The patent combines multiple information sources including user reviews, exclusive reviews by agents, API traffic data, and social networking data to generate composite ratings. This merging of diverse data sources compensates for the biases in individual sources while maintaining operational simplicity through automated data integration.
Solution Approach 2:
The system serves multiple functions by simultaneously collecting data from various sources (reviews, traffic data, social media), processing different types of information, and generating multiple dimensions of entity ratings. This multi-functional approach improves reliability without significantly increasing operational complexity.
2Reliability
If multiple data sources and features are combined to compute entity scores, then the reliability and comprehensiveness of the ratings improve, but the system complexity increases
Solution Approach 1:
The patent segments the complex scoring system into distinct modules: data collection from multiple sources, feature extraction from each source, data processing and normalization, and final score computation. This segmentation manages complexity by organizing the multi-source integration into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The system introduces intermediary processing layers including feature extraction modules and data normalization techniques that mediate between raw multi-source data and the final scoring mechanism. These intermediaries simplify the integration complexity by standardizing diverse data formats and extracting relevant features before final computation.
3Measurement precision
If comprehensive features from internal and external data sources are gathered, then the measurement precision of entity characteristics improves, but the loss of time and computational resources increases
Solution Approach 1:
The patent implements preliminary actions by pre-processing and storing data from multiple sources in structured formats, pre-computing features from historical data, and maintaining updated databases of entity information. This preliminary preparation reduces the time required for comprehensive evaluation when actual scoring is needed, as much of the data gathering and feature extraction is performed in advance.
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.