Group Recommendation System Using Social Graph and Location Signals
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Solution Overview
Problem
Users seeking destination recommendations for social groups face challenges in obtaining personalized suggestions that consider both individual and group preferences, as existing systems fail to effectively combine location-based signals and social graph information to provide tailored recommendations.
Innovation Solution
A system and method that identify a social group based on location-based signals and social graph information, generate a ranked group recommendation list by merging personal preference profiles, and display recommendations on user devices, ensuring that suggestions are tailored to the group's collective preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual personal preference profiles are used for recommendations, then personalization is improved, but group preference integration deteriorates
Solution Approach 1:
The patent merges individual personal preference profiles with group preference profiles to create comprehensive destination recommendations. The system combines data from multiple sources (individual check-ins, individual ratings, group activities) to generate a unified recommendation that reflects both personal and collective preferences, resolving the contradiction between personalization and group integration.
2Measurement precision
If location-based signals from multiple devices are processed, then group identification accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The patent introduces a social graph as an intermediary structure to manage and process location-based signals from multiple devices. The social graph organizes device relationships and enables efficient group identification without requiring complex direct processing of all device interactions, thus improving accuracy while managing system complexity.
3Measurement precision
If social graph information is combined with location signals, then recommendation accuracy is improved, but data processing complexity deteriorates
Solution Approach 1:
The patent segments the recommendation system into distinct modules: one for processing individual personal preference profiles, another for group preference profiles, and a third for integrating them. This segmentation allows the system to handle complex data processing in manageable parts, improving recommendation accuracy while controlling processing complexity through modular architecture.
Data Source
AI summary
A group recommendation provides end users in a social group a set of recommended destinations based on the combined personal preferences of the members of the social group. Members of a social group are identified using a combination of location based signals and social graph information in response to receiving a recommendation request. The group recommendation may be determined by combining the personal preferences associated with each member of the group into a master preference profile. Alternatively, the group recommendation may be determined by first calculating an individual recommendation list for each member of the social group and then calculating a composite score for each recommendation on the individual recommendation lists.