Social Affinity Review Prioritization in Online Storefronts
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Solution Overview
Problem
Online shoppers face challenges in identifying trustworthy reviews for content items, as most reviews are written by strangers, lacking the influence of trusted individuals.
Innovation Solution
A method prioritizes reviews based on social affinity information, matching user contacts with reviewers in a content item database, and displaying reviews from trusted individuals ahead of others, using a priority server to organize the presentation on an electronic device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reviews are displayed in traditional order (e.g., by recency or rating), then all reviews are visible, but reviews from trusted contacts are not prioritized
Solution Approach 1:
The patent introduces social affinity information as an intermediary factor between the reviewer and the user. This mediator (social affinity score) quantifies the trust relationship and uses it to reorder reviews, allowing trusted contacts' reviews to appear first without fundamentally changing the review display system architecture
Solution Approach 2:
The patent changes the sorting parameter from traditional criteria (recency, rating) to social affinity-based priority. By introducing a new parameter (social affinity score) that measures the strength of relationship between user and reviewer, the system reorders reviews to prioritize those from trusted contacts while maintaining visibility of all reviews
2Ease of operation
If reviews from all users are displayed equally, then review diversity is maintained, but influence of trusted recommendations is reduced
Solution Approach 1:
The patent applies local quality by differentiating the display treatment of reviews based on the relationship between user and reviewer. Reviews from high-affinity contacts receive special prioritization in the display order, while reviews from strangers maintain their traditional positioning. This localized differentiation enhances decision-making for trusted sources without losing overall review diversity
3Reliability
If social affinity matching is implemented, then review prioritization improves, but system processing complexity increases
Solution Approach 1:
The system uses existing social graph data and contact information that users already have in their devices to automatically calculate social affinity scores. The matching process leverages pre-existing data structures (contacts, social network connections) rather than requiring new data collection mechanisms, reducing the complexity burden while maintaining review prioritization functionality
Data Source
AI summary
A method is provided to display on an electronic device, prioritized reviews of content items offered in an online storefront; contact information associated the device includes social affinity information; contacts of the user of the device are matched to reviews of the content item within a content item database associated with the online storefront; social affinity between reviewers, who have a match user contacts, and the user of the device is used to prioritize the reviews; reviews are displayed on the electronic device UI with an indication of their priority based upon social affinity between the reviewer and the user of the device.