Enterprise AI Analysis
Empowering Next-Gen Recommendations with LLMs
CONREC pioneers a novel LLM framework for negative feedback modeling, leveraging advanced context-discerning modules and progressive training to accurately predict user disinterest and significantly enhance recommendation systems, especially in cold-start scenarios.
Executive Impact & Business Value
Explore the immediate and long-term business implications of integrating this AI advancement into your enterprise.
0
Candidate Accuracy Improvement
0
FHR@20 Improvement (7-day)
0
LIF@20 Improvement (Long-Tail Items)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Context-Discerning Negative Recommendation
CONREC introduces a novel LLM framework to model users' negative interests, utilizing specialized context-discerning modules to overcome limitations of traditional methods. It addresses inherent sparsity and positive feedback dominance by integrating hierarchical semantic ID representations and item-level alignment for nuanced context understanding.
Enterprise Process Flow
Multimodal Encoder
→
RQ-VAE (Semantic ID)
→
Bidirectional Translation
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Item Level Alignment
→
Progressive GRPO
→
Offline Filtering
| Feature | Traditional Method | CoNRec |
|---|---|---|
| Negative Interest Modeling |
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| Cold-Start Performance |
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| Context Understanding Bias |
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Progressive Training & Unbiased Rewards
CONREC employs a Progressive Group Relative Policy Optimization (GRPO) training paradigm that incrementally incorporates contextual information. It introduces a novel reward function and evaluation metrics based on multi-day future negative feedback and collaborative signals, mitigating biases from conventional next-negative-item prediction.
48%
Top-4 Negative Interest Coverage (7-day)
45%
Performance Drop Avoided with GRPO
Addressing Misalignment: From Next-Item to Future Items
Motivational studies reveal that the next negative feedback item only covers 7% of a user's top negative interest. Extending this to a 7-day future horizon increases coverage to 48%, significantly reducing noise. CONREC's novel reward function for GRPO is designed to capture these broader, more stable negative preferences, leading to more robust models.
13.6%
Lowest Forgetting Rate in Transfer Learning
Advanced ROI Calculator
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Implementation Roadmap
A phased approach to integrating CONREC, ensuring a smooth transition and measurable impact.
Phase 1: Data Preparation & Semantic ID Generation
Establish multimodal encoding and Residual Quantized Variational Autoencoder (RQ-VAE) for compact, informative Semantic IDs. Clean and filter negative feedback data relevant to user disinterests.
Phase 2: Context Understanding & Item Alignment
Fine-tune LLM for bidirectional translation between Semantic IDs and titles. Implement item-level alignment task to capture potential negative attributes, contrasting positive and negative signals.
Phase 3: Progressive GRPO Training
Initiate GRPO with a curriculum learning approach, starting with short negative feedback sequences and progressively incorporating full negative and positive historical data. Optimize with unbiased reward function accounting for future negative and collaborative items.
Phase 4: Offline Filtering Integration & Evaluation
Deploy CONREC as an offline filtering mechanism for target items. Reconstruct predicted Semantic IDs into embeddings and filter items exceeding a predefined similarity threshold, using FHR@20 and Candidate Accuracy for evaluation.
Phase 5: Continuous Improvement & Online A/B Testing
Monitor model performance, iterate on context features and training parameters. Conduct A/B tests in real-world production environments to validate and refine CONREC's impact on user experience and business metrics.
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