Social Graph Search Personalization via Affinity Group Ranking
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Solution Overview
Problem
Current search technologies do not effectively utilize relationship-based recommendations from social networks to personalize search results, failing to provide relevant information based on user relationships and affinity groups.
Innovation Solution
A system that aggregates and presents search results by leveraging relationship graphs from social networks, prioritizing recommendations from users closely related to the searcher, using user identifications and affinity groups to rank businesses and information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional search technologies are used, then search functionality is provided, but relationship-based recommendations from social networks are not utilized to personalize search results
Solution Approach 1:
The patent combines traditional search functionality with social network relationship data by merging the search engine's result generation process with the social graph's relationship information. This integration allows the system to simultaneously provide general search capabilities and personalized recommendations based on user relationships, resolving the contradiction between maintaining standard search function and incorporating relationship-based personalization.
Solution Approach 2:
The system introduces a social graph as an intermediary component that bridges traditional search and personalized recommendations. The social graph acts as a mediator that processes relationship data and feeds it into the search result generation process, enabling personalization without requiring fundamental changes to the core search technology.
2Reliability
If search results are presented without relationship-based recommendations, then simplicity is maintained, but relevance based on user relationships is reduced
Solution Approach 1:
The system segments the search process into distinct functional modules: the traditional search engine component, the social graph component, and the result integration component. This segmentation allows each module to operate independently with its own complexity management, enabling the system to incorporate relationship-based recommendations while maintaining overall architectural clarity and manageability.
Solution Approach 2:
The patent creates a universal search system that can operate in multiple modes: traditional search mode and personalized recommendation mode. The system's architecture is designed to handle both functionalities through a unified interface, allowing it to adapt to different user needs without requiring separate systems, thereby managing complexity while enhancing reliability.
3Adaptability or versatility
If relationship graphs from social networks are integrated, then personalized recommendations are provided, but processing complexity increases
Solution Approach 1:
The system performs preliminary processing of social network data by pre-building and maintaining a social graph structure that captures relationship information before search queries are executed. This preliminary action organizes relationship data in advance, allowing the search process to efficiently query pre-processed relationship information rather than processing raw social network data in real-time, thus reducing processing complexity during actual search operations.
Data Source
AI summary
Business, recommendation, and social relationship graph information for businesses may be received from a data source social networking website, where each business is recommended by users. The recommendation information may indicate users that recommend certain businesses. The social relationship graph information may indicate user-specific networks of social relationships on the social networking website. For a user query including business and affinity group selection criteria, business, recommendation, and social relationship graph information may be searched to select businesses that: match the business selection criteria; and are recommended by users having social relationships within a maximum degree of closeness with the querying user and being in an affinity group matching the affinity group selection criteria. The selected businesses may be ranked based on numbers of recommending users and social relationship graph information. A search result, with businesses indicated in a rank order and with business and recommendation information, may be provided.