Research Paper

Core Innovation

This paper introduces an entropy-based framework to quantify and manage uncertainty in user preferences within recommendation systems. It guides adaptive preference elicitation by selecting questions that maximize expected information gain and incorporates residual uncertainty into ranking and diversification, avoiding premature narrowing of options.

Why It Matters

E-commerce users often face uncertainty early in their search, leading to excessive questions or premature recommendations that reduce satisfaction. This system reduces unnecessary interactions and improves recommendation diversity and transparency, enhancing user experience and decision confidence. It scales across diverse shopping behaviors, making it valuable for large online retail platforms.

Potential Customers & Pain Points

• E-commerce platforms – High user query ambiguity causing poor recommendations
• Online marketplaces – Excessive user interactions leading to question fatigue
• Retailers – Low conversion due to unclear user preferences
• Consumer apps – Need for adaptive transparent recommendation systems

Business Model

SaaS platform licensing to e-commerce and marketplace operators with tiered pricing based on catalog size and query volume; potential for revenue share on increased conversions.

Competitive Landscape

• Amazon Personalize
• Google Recommendations AI
• Salesforce Einstein Recommendations

Implementation Challenges

• Integration complexity with existing recommendation infrastructures
• User acceptance of adaptive questioning interfaces
• Scalability of entropy calculations in large product catalogs

Validation Strategy

• Pilot deployment with mid-size e-commerce platforms to measure interaction reduction and conversion uplift
• A/B testing comparing entropy-guided system against baseline recommendation engines
• User studies to assess satisfaction and perceived transparency of recommendations