MMR for RAG: Low-Cost Chunk Limits Balance Relevance and Diversity

When a long document is split into many highly similar chunks, vector‑based top‑k retrieval tends to return multiple pieces from the same source, causing document dominance; applying a per‑document chunk limit together with Maximal Marginal Relevance (MMR) re‑ranking introduces diversity while preserving relevance, offering a low‑cost way to improve RAG answer quality.

ChunkingDPPDiversity

0 likes · 17 min read

MMR for RAG: Low-Cost Chunk Limits Balance Relevance and Diversity

DataFunTalk

Oct 6, 2020 · Artificial Intelligence

Optimizing Individual Diversity in Recommendation Systems: Architecture, Algorithms, and Practical Implementation

This article presents a comprehensive approach to improving recommendation system performance by optimizing individual diversity, detailing architectural layers, practical implementations of MMR and DPP algorithms, custom distance metrics, experimental results, and their impact on key business metrics such as click‑through and view rates.

Custom DistanceDPPDiversity Optimization

0 likes · 17 min read

Optimizing Individual Diversity in Recommendation Systems: Architecture, Algorithms, and Practical Implementation

58 Tech

Sep 7, 2020 · Artificial Intelligence

Optimizing Individual Diversity in Recommendation Systems: Architecture, MMR and DPP Implementation at 58 Tribe

This article presents a comprehensive study on improving individual diversity in recommendation systems by detailing architectural optimizations across recall, rule, and re‑ranking layers, explaining the principles and practical deployment of MMR and DPP algorithms, and demonstrating their impact on key business metrics through extensive experiments.

Algorithm OptimizationCustom DistanceDPP

0 likes · 18 min read

Optimizing Individual Diversity in Recommendation Systems: Architecture, MMR and DPP Implementation at 58 Tribe