Tip Generation System for Extracting Useful Advice from User Reviews
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Solution Overview
Problem
Users face overwhelming amounts of user-generated content when researching entities, such as products or locations, leading to missed valuable information due to the sheer volume of reviews, even when sorting by recency or popularity.
Innovation Solution
A system generates a set of tip templates based on patterns of n-grams from ground truth tips, which are then applied to user reviews to extract and rank candidate tips, filtered for usefulness and diversity, providing concise and relevant advice without requiring users to read through numerous reviews.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users read through numerous user reviews to research an entity, then they may find valuable information, but they spend excessive time and become overwhelmed by the volume of content
Solution Approach 1:
The system extracts useful tips from user reviews by applying tip templates to identify and pull out actionable advice. This extraction process separates valuable information from the overwhelming volume of reviews, allowing users to get essential insights without reading every review.
Solution Approach 2:
The patent introduces an intermediary system that acts as a mediator between users and reviews. This system processes reviews through tip templates and classification algorithms to generate condensed tip sets, serving as an intermediary layer that transforms raw review data into digestible, actionable advice.
2Ease of operation
If users sort reviews by most recent or most popular, then they can prioritize certain reviews, but they still miss useful information in other reviews
Solution Approach 1:
The tip template system is designed to be universal and apply to all reviews regardless of their sort order. The templates can identify useful tips in any review, making the system multi-functional in processing different types and positions of reviews, thereby capturing valuable information that would otherwise be missed.
Solution Approach 2:
The system performs preliminary action by pre-processing all reviews through tip template matching before user viewing. This preliminary extraction of tips from all reviews (not just sorted ones) ensures that valuable information is captured in advance, regardless of the user's eventual review selection or sorting preferences.
3Loss of information
If the system extracts tips from all user reviews, then comprehensive information is captured, but the complexity of processing increases
Solution Approach 1:
The tip extraction system is segmented into modular components: tip templates, classification algorithms, and filtering mechanisms. This segmentation allows the complex task of processing all reviews to be broken down into manageable, independent modules that can be processed efficiently and maintained separately.
Solution Approach 2:
The system uses parameter changes in the form of tip templates with wildcards and patterns to efficiently match and extract tips. By changing the parameters of template matching (n-gram patterns, wildcard positions, repetition metrics), the system can comprehensively process reviews while maintaining manageable complexity through configurable parameters rather than hard-coded logic.
Data Source
AI summary
One or more computing devices, systems, and/or methods for generating a set of tips for an entity are provided. For example, users may create user generated content describing an entity, such as a user review for a consumer good, a location, an event, etc. Because a user may be unable to read and digest all of the user reviews for the entity, the user may merely read a few user reviews, and thus miss out on useful information. Accordingly, tip templates, indicative of how tips are linguistically/grammatically constructed, are applied to the user reviews to automatically extract a set of tips for the entity (e.g., “make sure to bring a rain jacket”). The set of tips may be filtered to remove undesirable tips, ranked based upon usefulness, and/or diversified to remove redundant tips. In this way, a set of useful tips may be provided to the user.