Group Recommendation System for Multi-User Activity Clustering
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Solution Overview
Problem
Existing search engine solutions fail to provide relevant recommendations for activities involving multiple users, as they primarily focus on individual user preferences and locations, neglecting the preferences and locations of other participants, which reduces the relevance and effectiveness of the recommendations.
Innovation Solution
The system tracks status information of multiple users, identifies clusters based on proximity and social dynamics, and provides activity recommendations customized to the profiles and behavioral characteristics of users within these clusters, including incentives to encourage cluster members to accept the recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If personalized search and local search are used to provide recommendations based on individual user preferences and location, then the relevance of recommendations for individual users is improved, but the relevance of recommendations for group activities involving multiple users deteriorates
Solution Approach 1:
The patent combines multiple user profiles and preferences into a unified group recommendation system. It merges individual user data (locations, preferences, behavior patterns) to generate recommendations that satisfy the collective needs of group members, resolving the contradiction between individual precision and group versatility.
Solution Approach 2:
The recommendation system is designed to serve multiple functions: it can provide personalized recommendations for individual users while simultaneously generating group-oriented recommendations. The system adapts its output based on whether the query is individual or group-based, making it universally applicable to different user scenarios.
2Ease of operation
If recommendations are personalized to a single user conducting the search, then the user experience for that individual is improved, but the likelihood of group acceptance and participation deteriorates
Solution Approach 1:
The system applies different recommendation strategies to different users within a group based on their individual preferences and characteristics. Each user receives tailored recommendations that consider their specific tastes, while the overall group recommendation finds common ground, ensuring both individual satisfaction and group acceptance.
Solution Approach 2:
The system incorporates feedback from multiple users to refine and adjust recommendations. By considering responses and interactions from group members, the system iteratively improves recommendations to achieve broader group acceptance while maintaining individual user experience quality.
Data Source
AI summary
Methods, systems, and computer program products for providing recommendations for an activity to a user are provided. In one method, the method tracks status information of a plurality of users, and detects a trigger for providing recommendations for an activity. In response the trigger, the method identifies a cluster of users based on the status information of the users. The method further retrieves profiles and behavioral characteristics of the users in the identified cluster, and provides one or more recommendations for the activity to the user based, at least in part, upon the behavioral characteristics and the profiles.