Idea
Recommendation model enhancing user experience by accurately identifying and leveraging negative preferences in large-scale systems.
Research Paper
Core Innovation
This paper introduces CoNRec, the first large language model framework specifically designed for negative feedback modeling with context-discerning modules. It replaces text-based item descriptions with semantic ID representations and incorporates an item-level alignment task to deepen semantic understanding of negative feedback. Additionally, it proposes a progressive training paradigm and a novel reward function to better align model predictions with true user negative preferences over time.
Why It Matters
Understanding negative user preferences is critical for improving recommendation relevance and reducing negative feedback rates, which directly impacts user satisfaction and retention. By effectively modeling dislikes, platforms can avoid unwanted recommendations, enhancing user trust and engagement. This approach scales across industries relying on personalized content delivery, transforming how negative signals are utilized in recommendation workflows.
Market Size (TAM)
$20–50B TAM for recommendation systems; $5–10B SAM from streaming, e-commerce, and social media platforms. Driven by increasing demand for personalized user experiences and reduction of negative feedback.
Potential Customers & Pain Points
- Streaming platforms – High negative feedback rates reducing user retention
- E-commerce sites – Difficulty capturing and using negative preferences
- Social media networks – Need to improve content relevance and reduce user churn
- Online education platforms – Challenges in filtering disliked content to enhance learning experience
Business Model
SaaS platform offering API access to CoNRec models for integration into existing recommendation systems, with tiered pricing based on usage volume and customization level.
Competitive Landscape
- Google Recommendations AI
- Amazon Personalize
- Microsoft Azure Personalizer
- Alibaba Cloud Recommendation
Implementation Challenges
- Sparsity and noise in negative feedback data limiting model accuracy
- Integration complexity with existing recommendation pipelines
- User privacy concerns around detailed behavioral data
- Adoption resistance due to shifting focus from positive to negative feedback modeling
Validation Strategy
- Pilot deployments with streaming and e-commerce partners to measure reduction in negative feedback rates
- A/B testing comparing CoNRec-enhanced recommendations against baseline models
- User engagement and retention analysis post-integration
- Iterative model refinement based on real-world feedback and multi-day negative signal tracking
Research Paper Overview
CoNRec: Context-Discerning Negative Recommendation with LLMs
Summary
CoNRec addresses the challenge of modeling user negative preferences in recommendation systems by leveraging large language models with context-discerning modules. It improves understanding of negative feedback through semantic ID representation, an item-level alignment task, and a progressive training paradigm balancing positive and negative contexts. The approach also introduces a novel reward function and evaluation metric based on multi-day future negative feedback to better align with true user dislikes.