Social Network Segmented Recommendation Interface
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Solution Overview
Problem
Existing online recommendation systems often provide generic and lengthy lists of recommendations that are not effective in persuading visitors to make a purchase, as they do not account for personalized social network interactions.
Innovation Solution
A system and method that utilizes a server system with processing logic to output detailed pages with selectable indicators for content filtered by social networks, allowing visitors to receive personalized recommendations based on their social network members' purchases, views, and reviews, alongside unfiltered and mixed recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic recommendations are provided based on what other customers have purchased or viewed, then the system can provide recommendations without requiring social network integration, but the recommendations become too long and ineffective to persuade visitors to make a purchase
Solution Approach 1:
The patent segments the recommendation list by social network connections, separating recommendations from different social circles into distinct sections. This allows the system to maintain a comprehensive set of recommendations while organizing them in a more manageable and effective format that prevents overwhelming visitors with a single long unfiltered list.
Solution Approach 2:
The patent applies local quality by providing different recommendation strategies for different segments of the recommendation list. Recommendations from close social connections (friends, family) are presented differently from those of more distant connections, with each segment tailored to its specific social context and relevance to the visitor.
2Adaptability or versatility
If recommendations are filtered by social network, then the recommendations become more personalized and relevant, but the system complexity increases due to social network integration requirements
Solution Approach 1:
The patent implements a universal recommendation system that can operate in multiple modes: it can provide recommendations based on social network connections when available, or fall back to generic recommendations when social network data is not available. This multi-functionality allows the system to adapt to different scenarios without requiring complex social network integration in all cases.
Solution Approach 2:
The patent introduces an intermediary layer that manages the integration between the recommendation engine and social network data. This intermediary handles the complexity of social network integration by providing a standardized interface, filtering and processing social network information before presenting it to the recommendation algorithm, thereby isolating the core recommendation system from social network complexity.
3Adaptability or versatility
If a comprehensive list of recommendations is provided, then all possible options are available to visitors, but the list becomes too long to be useful and effective
Solution Approach 1:
The patent divides the comprehensive recommendation list into multiple segmented sections based on social network relationships. Instead of presenting one long unfiltered list, the system organizes recommendations into manageable segments (e.g., recommendations from close friends, recommendations from acquaintances, recommendations from community members), making the overall list more navigable and effective while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies partial action by selectively presenting recommendations based on their relevance and source. Rather than displaying every possible recommendation equally, the system prioritizes and highlights recommendations from more trusted or relevant social connections, presenting a curated subset that maintains comprehensiveness while avoiding excessive length.
Data Source
AI summary
A method of providing recommendations is disclosed and includes receiving a selection of an offering via an online site. The method also includes outputting a detail page related to the offering via the online site. The detail page includes a first selectable indicator corresponding to a social network of a visitor and a second selectable indicator corresponding to unfiltered content. The method also includes outputting at least one recommendation via the detail page. Each recommendation is associated with a member of the social network when input received via the online site indicates a selection of the first selectable indicator.