Transformer Model Review Ranking for Host Relevance
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Solution Overview
Problem
Online booking systems face challenges in effectively ranking user reviews that discuss host users, as existing methods fail to prioritize relevant reviews based on their content and sentiment, leading to suboptimal decision-making for guests.
Innovation Solution
A machine learning-based system utilizing a transformer model to generate host and listing relevancy scores, combined with sentiment and name detection models, to prioritize reviews that discuss the host user, ensuring relevant and sentiment-matched reviews are displayed prominently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing review ranking methods are used, then the system is simple to operate, but the review ranking precision and relevance are insufficient
Solution Approach 1:
The patent introduces machine learning models (transformer model, sentiment analysis model, name detection model) as intermediary components that process review data and generate ranking scores. These models act as mediators between the raw review data and the final ranking output, enabling precise relevance assessment without requiring complex manual processing rules.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based ranking mechanisms with machine learning-based automated ranking systems. The transformer model substitutes for manual content analysis, while the sentiment and name detection models automate the evaluation of review characteristics, eliminating the need for complex manual sorting procedures.
2Loss of information
If all reviews are displayed equally, then the system is easy to implement, but the information quality for guest decision-making is reduced
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different review attributes. The system separately analyzes and weights different aspects of reviews (host relevance, listing relevance, sentiment, name mentions) according to their specific importance for guest decision-making, rather than treating all review content uniformly.
Solution Approach 2:
The patent performs preliminary action by pre-processing and analyzing review data before it is displayed to guests. The machine learning models continuously evaluate review relevance, sentiment, and key characteristics in advance, so that when guests access the system, the reviews are already ranked and prepared for optimal presentation.
3Measurement precision
If reviews are ranked without considering host relevance, then the ranking process is fast, but the decision-making accuracy for guests is reduced
Solution Approach 1:
The patent segments the review analysis process into distinct independent components: a transformer model for host and listing relevance scoring, a sentiment analysis model for tone evaluation, and a name detection model for identifying host mentions. Each segment processes specific aspects of the review data in parallel, improving overall efficiency while maintaining comprehensive analysis.
Solution Approach 2:
The transformer model serves multiple functions simultaneously: it analyzes host relevance, listing relevance, and generates overall review scores all within a single processing operation. This multi-functionality reduces the need for separate sequential analysis steps, thereby decreasing processing time while maintaining high measurement precision.
Data Source
AI summary
Systems and methods herein describe ranking reviews that specify details of a host user. The described systems and methods access a set of reviews associated with a host user and listing data, and for each review in the set of reviews, generate a first relevancy score associated with the host user and a second relevancy score associated with the listing data using a transformer machine learning model, determine a first rank score for the review based on the first relevancy score. The systems and methods cause display of the set of reviews in an order based on the associated first rank score on a graphical user interface of a computing device.