Social Graph Recommendations for Event Planning
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Solution Overview
Problem
Social networking systems face challenges in recommending content objects and event planning due to varying user preferences and expectations, requiring efficient methods to suggest relevant content and resources.
Innovation Solution
A social networking system that generates recommendations based on user location, social graph data, and catalog information, using affinity coefficients and lookalike user data to suggest personalized content and event planning options, such as food and seating arrangements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system uses traditional recommendation methods without social graph data, then the system complexity is lower, but the recommendation accuracy and personalization capability deteriorate
Solution Approach 1:
The patent introduces a social graph data structure as an intermediary between user profiles and recommendation algorithms. This social graph captures relationships and interactions between users, serving as a mediator that enables more accurate recommendations without requiring complete restructuring of the recommendation system. The social graph acts as a bridge that translates social interactions into recommendation signals.
Solution Approach 2:
The patent adds a new dimension to the recommendation system by incorporating social relationship data alongside traditional user profile attributes. This transforms the recommendation problem from analyzing single-user characteristics to analyzing multi-dimensional user-social context, enabling more nuanced and accurate recommendations through additional informational dimensions.
2Ease of operation
If the system queries extensive user data to provide personalized recommendations, then the recommendation quality improves, but the resource consumption and processing time increase
Solution Approach 1:
The patent pre-computes and stores aggregated social graph metrics (such as connection counts, interaction frequencies, and relationship strengths) during user activities. This preliminary action eliminates the need for real-time computation of these metrics when generating recommendations, significantly reducing processing resources required during actual recommendation queries while maintaining high recommendation quality.
Solution Approach 2:
The patent extracts and stores only the most relevant social graph features needed for recommendations, rather than querying the entire social graph data structure. By identifying and extracting key metrics such as number of connections, interaction patterns, and relationship types, the system reduces the volume of data processed during recommendation generation while preserving recommendation accuracy.
Data Source
AI summary
In one embodiment, a method includes receiving an indication that a user has created an event object with a plurality of event parameters. The method also includes accessing a social graph with nodes and edges, accessing a database associated with third-party food-service providers. The database includes several food-service parameters. The method also includes generating food-service recommendations for the event. The food service recommendations are based on (1) at least some of the event parameters, (2) at least some of the nodes and edges in the social graph, and (3) at least some of the plurality of food-service parameters. The method also includes sending the food-service recommendations in ranked order to the a client system associated with the user of the online social network.