Idea
A personalized recommendation platform using determinantal point processes to deliver diverse cultural activity suggestions for users.
Research Paper
Core Innovation
This paper introduces personalized Determinantal Point Processes that incorporate user preferences directly into the similarity kernel to balance relevance and diversity. Unlike prior work, it provides practical implementations and evaluates trade-offs with both offline and online metrics. The approach enables scalable, diverse recommendations tailored to individual users.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: global cultural and entertainment recommendation markets expanding with digital personalization.
Potential Customers & Pain Points
- Cultural event platforms needing diverse recommendations
- Online ticketing services seeking personalized user engagement
- Museums and galleries aiming to increase visitor variety
Business Model
SaaS platform licensing personalized DPP recommendation APIs to cultural and event platforms; potential revenue from usage fees and customization services.
Competitive Landscape
- Spotify
- Eventbrite
- Netflix
Implementation Challenges
- Complexity of tuning relevance-diversity trade-offs
- Integration with existing recommendation systems
- User privacy and preference data collection
Validation Strategy
- Deploy prototype with partner cultural platform for A/B testing
- Measure user engagement and diversity metrics online
- Iterate model based on feedback and scale to additional clients
Research Paper Overview
Diversified recommendations of cultural activities with personalized determinantal point processes
Summary
This paper explores using personalized Determinantal Point Processes (DPPs) to generate diverse and relevant cultural activity recommendations by weighting user preferences in the similarity kernel. It presents implementations of personalized DPP sampling, evaluates relevance-diversity trade-offs with offline and online metrics, and offers practical insights for production use. The full codebase is released for reproducibility.