Cognitive Event Recommendation System for Distributed Groups
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computing systems lack the ability to effectively recommend convenient event opportunities to groups of geographically distributed individuals who share social connections, considering factors like geographic location, interests, and cost, while also learning from feedback and daily activities.
Innovation Solution
A cognitive system that identifies and suggests ranked lists of event opportunities by matching entity selection criteria with event criteria, using machine learning to refine suggestions based on feedback and incorporating data from IoT devices, such as smartphones and sensors, to optimize convenience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system considers multiple factors (geographic location, interests, cost, feedback) to provide personalized recommendations, then the quality and personalization of recommendations is improved, but the system complexity increases
Solution Approach 1:
The system segments the recommendation process into distinct functional modules: entity identification module, event opportunity identification module, matching module, and ranking module. Each module handles a specific aspect of the recommendation task, processing entity data, event data, matching criteria, and ranking algorithms separately. This modular segmentation allows the system to manage complexity through organized, independent components while maintaining personalized recommendations through their coordinated interaction.
Solution Approach 2:
The system implements a universal matching framework that handles multiple types of criteria (geographic location, interests, cost, feedback) through a single multi-functional matching mechanism. The matching module universally processes diverse input types using standardized algorithms, and the ranking module universally applies convenience scoring across different event types and entity groups. This multi-functionality enables personalized recommendations without requiring separate specialized systems for each factor.
2Measurement precision
If the system processes data from multiple sources (IoT devices, feedback, entity profiles) to optimize convenience, then the accuracy of recommendations is improved, but the data processing requirements and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing entity profiles, event data, and historical feedback in structured formats before actual recommendation generation. Entity selection criteria and event criteria are pre-defined and stored, allowing the matching and ranking processes to operate on prepared data rather than raw information. This preliminary structuring of data from multiple sources reduces computational load during real-time recommendation while maintaining high accuracy through comprehensive data integration.
3Adaptability or versatility
If the system provides comprehensive event opportunities for geographically distributed groups, then the versatility of recommendations is improved, but the difficulty of matching and ranking increases
Solution Approach 1:
The system applies local quality by tailoring the matching and ranking process to specific group characteristics and event types. The matching module adapts its criteria based on the particular entity group being analyzed, applying relevant factors (geographic proximity, shared interests, budget constraints) with appropriate weightings for each specific recommendation context. The ranking module similarly adjusts convenience scoring based on local group dynamics and event specifics, rather than applying uniform rules universally. This localized adaptation simplifies the matching process for each specific case while maintaining comprehensive coverage across diverse scenarios.
Data Source
AI summary
Embodiments for implementing intelligent recommendations of convenient event opportunities by a processor. A group of entities may be identified for one or more event opportunities or the one or more event opportunities may be identified for the group of entities according to one or more entity selection criteria and one or more event criteria. The one or more event opportunities and the group of entities may be matched according to a level of convenience for attending the one or more event opportunities of the group of entities. The one or more matching event opportunities may be ranked and suggested to the group of entities.