E-commerce Review Link Aggregation and Ranking
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Solution Overview
Problem
E-commerce websites face challenges in enhancing customer online shopping experiences by effectively leveraging user-provided content, such as reviews, to suggest associated products or elements, as existing methods lack efficient aggregation, sorting, and display of relevant links within these reviews.
Innovation Solution
Implementing a system that identifies, ranks, and displays links within user-provided content, such as customer reviews, based on predetermined criteria like frequency and ratings, to suggest associated products or elements, improving customer experience by capitalizing on customer associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system displays all links found in customer reviews, then the quantity of information provided to customers increases, but the relevance and quality of product suggestions deteriorates due to lack of filtering
Solution Approach 1:
The patent extracts only the most relevant links from customer reviews by applying filtering criteria (frequency thresholds, rating requirements, recency filters) to separate useful product suggestions from irrelevant or spammy content, displaying only the extracted high-quality links
Solution Approach 2:
The patent applies different quality standards and filtering criteria to different types of links and review contexts, allowing high-frequency, highly-rated links to be displayed prominently while filtering out low-quality content, creating localized quality variations in the displayed results
2Measurement precision
If the system implements complex filtering and ranking algorithms to improve link quality, then the relevance of suggestions improves, but the computational complexity and processing time increases
Solution Approach 1:
The patent segments the link filtering process into distinct stages: initial frequency-based filtering, rating threshold application, recency filtering, and final ranking, allowing each stage to handle a specific aspect of quality assessment independently and efficiently
Solution Approach 2:
The patent changes key parameters such as frequency thresholds, minimum rating values, and time-window durations to optimize the balance between link quality and processing efficiency, adjusting these parameters based on business requirements and performance metrics
3Loss of information
If the system processes and analyzes all customer reviews to extract links, then the completeness of product associations improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing customer reviews to identify and cache frequently mentioned product links, building indexes and frequency tables in advance so that when a review is displayed, the associated links can be quickly retrieved and ranked without re-analyzing all review data
Solution Approach 2:
The patent processes a representative sample or subset of reviews to extract links, using frequency thresholds to identify the most common product associations without needing to analyze every single review, achieving sufficient completeness with reduced processing effort
Data Source
AI summary
Techniques described enable an entity, such as a company employing an e-commerce website, to leverage user-provided content, such as customer reviews of an item, to better customers' shopping experiences. To do so, customer reviews pertaining to an item may be examined to determine if the reviews contain links to other items. These links within the customer reviews may then be aggregated and sorted (e.g., ranked) according to certain criteria. The links may be sorted based on a number of times that the links are used in the reviews and/or on ratings of the items associated with the links. One or more of the links may then be displayed on the website. For instance, these links may appear on an item review page adjacent the customer reviews. Customers navigating to the item review page may then peruse the customer reviews as well as the displayed links that customers have used within the reviews.