Personalized Search Result Cards Using Social Graph Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing online recommendation systems fail to provide reliable content that takes into account the characteristics of the user making the search query, often prioritizing ratings over relevance and neglecting the recipient's preferences.
Innovation Solution
A social networking system that generates search results by considering user characteristics, including demographics, interactions, and preferences, through a recommendation engine that uses a search dictionary, social graph, user profile data, and content store to provide personalized result cards with images, comments, and location-based maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing online recommendation systems prioritize ratings over user characteristics, then the simplicity of the recommendation algorithm is maintained, but the reliability and relevance of the recommended content deteriorates
Solution Approach 1:
The system segments the recommendation process into multiple independent modules: a search query receiver, a user characteristic analyzer that processes demographics/interactions/preferences, a content relevance evaluator, and a result generator. This modular architecture allows each component to specialize in one aspect while maintaining overall system manageability despite increased complexity.
Solution Approach 2:
The patent introduces intermediary components including a search dictionary that mediates between user queries and content databases, and a social graph structure that acts as an intermediary to model relationships between users and content. These intermediaries enable the system to process user characteristics without requiring complete reengineering of the recommendation engine.
2Measurement precision
If the system integrates user characteristics and social interactions into search algorithms, then the accuracy of search results improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user characteristics, demographics, and interaction patterns in structured formats (user profiles, social graphs) before actual search queries are executed. This pre-computation reduces the computational burden during real-time search operations, allowing accurate results without excessive resource consumption during query processing.
Solution Approach 2:
The patent applies local quality by selectively processing only the relevant user characteristics and social interaction data needed for each specific search query, rather than analyzing all available user data uniformly. The system adapts the depth and scope of analysis based on the specific query context, reducing unnecessary computational overhead while maintaining accuracy for relevant factors.
3Adaptability or versatility
If the system provides personalized result cards with multiple constraints (topic type, location, social interaction, time), then the relevance of results to user needs improves, but the complexity of query processing and result filtering increases
Solution Approach 1:
The system segments the multi-constraint filtering process into separate handling modules: one module processes topic type constraints, another handles location-based filtering, a third manages social interaction constraints, and a fourth processes time-based filters. Each module independently evaluates its specific constraint type and returns filtered results, which are then combined. This segmentation makes the complex multi-constraint system more manageable and maintainable.
Solution Approach 2:
The patent implements a universal result card structure that can accommodate multiple constraint types and personalization options through a standardized interface. The result card template is designed to universally handle various constraint combinations (topic, location, social interaction, time) without requiring separate processing logic for each constraint type, reducing overall system complexity despite the versatility provided.
Data Source
AI summary
Exemplary methods, apparatuses, and systems for processing a search query of a user are detailed. For example, a search query may be received from a user at a social networking system, processed to generate a search result of a plurality of entity result cards and each result card that each include a plurality of order comments about the entity, a plurality of ordered images associated with the entity, contact information for the entity, wherein the comments and images take into account information about the user stored at the social networking system, and a result send to the user.