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AI Algorithms for Personalized Artist and Venue Recommendations

Overview

A personalized AI recommendation system that matches users with suitable artists and venues using real-time data, agent workflows, and intelligent ranking.

Finding the right artist or venue for an event can be difficult because decisions depend on many factors such as availability, location, popularity, audience needs, budget fit, and diversity. Manual research takes time and may not always give the most relevant options.

This project solves that problem by building an AI-powered recommendation system that suggests artists and venues based on real-time needs and relevant data. It uses agent-based workflows with Agno and CrewAI to analyze availability, popularity, region, and matching criteria.

The system also uses browser automation and Firecrawl for real-time data scraping. A query agent processes the collected data and delivers the most suitable recommendations, helping users make faster and smarter event planning decisions.

Key benefits

  1. Recommends artists and venues based on user needs
  2. Uses real-time data scraping for updated results
  3. Considers availability: popularity, region, and diversity
  4. Uses AI agents to improve recommendation quality
  5. Helps reduce manual research time for event planning

What's included

  1. Artist and venue recommendation workflow
  2. Real-time data scraping system
  3. Query agent for relevant data filtering
  4. FastAPI backend service
  5. AWS EC2-based deployment support

Use Cases

  1. Event artist recommendation
  2. Venue matching and selection
  3. Entertainment booking support
  4. Real-time event planning research
  5. Personalized recommendation platforms

Key deliverables

  1. AI-powered recommendation engine
  2. Agent-based data analysis workflow
  3. Real-time scraping pipeline
  4. Query agent for result ranking
  5. FastAPI backend API
  6. AWS-ready deployment structure
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