Update README.md

README.md

@@ -142,6 +142,40 @@ Although these NDCG values are lower than the baseline, this is expected for sev
 - NDCG is not well aligned with multi-rule personalization behavior  
 - User4 performance is limited by scarcity of relevant recipes
 
+---
+## Risks and Bias
+
+The models are trained on the Food.com dataset, which has known biases:
+- **Regional bias**: Western and American cuisines dominate the dataset, leading to potential under-representation of other regions.
+- **Popularity bias**: Highly rated or frequently interacted recipes are over-represented.
+- **Cold-start leakage risk**: Although user interactions are synthetic, overlapping ingredient-parent structures between train/test may create mild information leakage, potentially inflating baseline metrics.
+
+These biases may affect recommendation diversity and fairness across different cuisines or dietary groups.
+
+---
+
+## Cost and Latency
+
+All models are based on **XGBRanker**, which runs efficiently on CPU:
+- **Inference latency**: Approximately 1–5 ms per recipe for ranking (measured on a laptop CPU, single thread).
+- **Training cost**: Training each user profile model on 5,000 interactions takes less than 2 minutes on CPU.
+
+The approach is designed for real-time personalization in lightweight interfaces (e.g., Hugging Face Spaces).
+
+---
+
+## Usage Disclosure
+
+**Intended Uses**
+- Academic and educational research on personalized recommendation
+- Cold-start personalization experiments
+- Recipe recommendation for diverse dietary profiles
+
+**Not Intended For**
+- Medical or dietary decision-making
+- Real-world deployment without additional bias mitigation
+- High-stakes personalization where fairness across demographic groups is critical
+
 ---
 
 ## Citation