Research Paper

Core Innovation

This paper presents IceBreaker, a novel framework that treats conversation initiation as a two-step process: resonance-aware interest distillation to identify user triggers from session summaries, and interaction-oriented starter generation optimized for personalized preference alignment and engagement maximization. This approach uniquely addresses the cold-start problem in conversation initiation, unlike prior work focused on ongoing dialogues.

Why It Matters

Conversational agents struggle to initiate dialogue when users lack explicit queries, causing engagement drop-offs. IceBreaker addresses this by generating personalized starters that guide users into conversations, improving active usage and interaction rates. This enhances user retention and scales engagement for large-scale conversational platforms.

Potential Customers & Pain Points

• Conversational AI platforms – Low user engagement at conversation start
• Customer support bots – Difficulty initiating user interaction
• Social chat apps – High drop-off before first message
• Virtual assistants – Limited proactive conversation initiation.

Business Model

Licensing the IceBreaker technology as an API or SDK to conversational AI providers and enterprises, with tiered pricing based on usage and customization levels.

Competitive Landscape

• Google Dialogflow
• Microsoft Bot Framework
• Rasa
• OpenAI ChatGPT APIs

Implementation Challenges

• Accurately inferring user interests without explicit input
• Balancing personalization with privacy concerns
• Integrating with diverse conversational platforms
• Measuring long-term engagement impact

Validation Strategy

• Conduct extended A/B testing across multiple conversational platforms
• Measure user engagement metrics including active days and click-through rates
• Collect qualitative user feedback on conversation quality
• Iterate model based on real-world deployment data