Update README.md

README.md

@@ -1,3 +1,18 @@
+---
+license: mit
+datasets:
+- hiiamkik/kuairec-embeddings
+language:
+- en
+pipeline_tag: tabular-classification
+tags:
+- recommender-system
+- pytorch
+- lightgcn
+- mmoe
+- faiss
+- causal-inference
+---
 # GraphRecSys
 
 Production-style recommendation system that combines graph retrieval, causal debiasing, multi-objective ranking, calibrated probabilities, vector search, and low-latency serving.
@@ -369,5 +384,4 @@ This repository is structured to show senior-level recommender systems judgment:
 
 ## Resume Summary
 
-Built an end-to-end production-style recommendation system using PyTorch, PyTorch Geometric, FAISS, Redis, FastAPI, and MLflow. Implemented LightGCN retrieval with IPS debiasing, MMoE multi-task ranking, Platt calibration, MMR diversity reranking, vector-search serving, offline A/B simulation, and Pareto frontier analysis for engagement/value trade-off optimization.
-
+Built an end-to-end production-style recommendation system using PyTorch, PyTorch Geometric, FAISS, Redis, FastAPI, and MLflow. Implemented LightGCN retrieval with IPS debiasing, MMoE multi-task ranking, Platt calibration, MMR diversity reranking, vector-search serving, offline A/B simulation, and Pareto frontier analysis for engagement/value trade-off optimization.