Add dataset card

README.md

@@ -1,37 +1,148 @@
 ---
-dataset_info:
-  features:
-  - name: instruction
-    dtype: string
-  - name: input
-    dtype: string
-  - name: gt_item_id
-    dtype: string
-  - name: gt_title
-    dtype: string
-  - name: gt_rating
-    dtype: float64
-  - name: user_id
-    dtype: string
-  - name: user_type
-    dtype: string
-  - name: candidate_set
-    dtype: string
-  - name: source_domain
-    dtype: string
-  - name: target_domain
-    dtype: string
-  - name: candidate_count
-    dtype: int64
-  splits:
-  - name: val
-    num_bytes: 145128496
-    num_examples: 12000
-  download_size: 50456581
-  dataset_size: 145128496
-configs:
-- config_name: default
-  data_files:
-  - split: val
-    path: data/val-*
+license: apache-2.0
+task_categories:
+  - text-generation
+language:
+  - en
+tags:
+  - recommendation
+  - cross-domain
+  - evaluation
+  - kitrec
+  - validation-data
+size_categories:
+  - 10K<n<100K
 ---
+
+# KitREC Validation Dataset - Set A
+
+Validation dataset for the KitREC (Knowledge-Instruction Transfer for Recommendation) cross-domain recommendation system.
+
+## Dataset Description
+
+This validation dataset is designed for evaluating fine-tuned LLMs on cross-domain recommendation tasks during training. It uses the same users as the test set but allows for validation monitoring.
+
+### Dataset Summary
+
+| Attribute | Value |
+|-----------|-------|
+| **Candidate Set** | Set A (Hybrid (Hard negatives + Random)) |
+| **Total Samples** | 12,000 |
+| **Source Domain** | Books |
+| **Target Domains** | Movies & TV, Music |
+| **User Types** | 4 (2 per target domain) |
+| **Rating Range** | 1.0 - 5.0 |
+| **Mean Rating** | 4.171 |
+
+### Set A vs Set B
+
+- **Set A (Hybrid)**: Contains hard negative candidates + random candidates for challenging evaluation
+- **Set B (Random)**: Contains only random candidates for fair baseline comparison
+
+Both sets use the same ground truth items but differ in candidate composition.
+
+### User Type Distribution
+
+| User Type | Count | Percentage |
+|-----------|-------|------------|
+| overlapping_books_movies | 3,000 | 25.00% |
+| overlapping_books_music | 3,000 | 25.00% |
+| source_only_movies | 3,000 | 25.00% |
+| source_only_music | 3,000 | 25.00% |
+
+
+### User Type Definitions
+
+| User Type | Description |
+|-----------|-------------|
+| `overlapping_books_movies` | Users with history in both Books and Movies & TV |
+| `overlapping_books_music` | Users with history in both Books and Music |
+| `source_only_movies` | Users with ONLY Books history (extreme cold-start for Movies) |
+| `source_only_music` | Users with ONLY Books history (extreme cold-start for Music) |
+
+## Dataset Structure
+
+### Data Fields
+
+- `instruction` (string): The recommendation prompt including user history
+- `input` (string): Candidate items for recommendation (100 items per sample)
+- `gt_item_id` (string): Ground truth item ID
+- `gt_title` (string): Ground truth item title
+- `gt_rating` (float): User's actual rating for the ground truth item (1-5 scale)
+- `user_id` (string): Unique user identifier
+- `user_type` (string): User category (4 types)
+- `candidate_set` (string): A or B
+- `source_domain` (string): Books
+- `target_domain` (string): Movies & TV or Music
+- `candidate_count` (int): Number of candidate items (100)
+
+### Data Split
+
+| Split | Samples | Description |
+|-------|---------|-------------|
+| val | 12,000 | Validation set for training monitoring |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load validation dataset
+dataset = load_dataset("Younggooo/kitrec-val-seta")
+
+# Access validation data
+val_data = dataset["val"]
+print(f"Validation samples: {len(val_data)}")
+
+# Example: Filter by user type
+overlapping_movies = val_data.filter(
+    lambda x: x["user_type"] == "overlapping_books_movies"
+)
+print(f"Overlapping Movies users: {len(overlapping_movies)}")
+
+# Example: Calculate metrics by user type
+from collections import defaultdict
+user_type_metrics = defaultdict(list)
+for sample in val_data:
+    user_type_metrics[sample["user_type"]].append(sample["gt_rating"])
+```
+
+## Validation vs Test Data
+
+| Aspect | Validation (this dataset) | Test |
+|--------|---------------------------|------|
+| Samples | 12,000 | 30,000 |
+| User Types | 4 types | 10 types |
+| Purpose | Training monitoring | Final evaluation |
+| Usage | During training | After training |
+
+### Why Separate Validation Set?
+
+- Monitors training progress without data leakage
+- Enables early stopping based on validation loss
+- Validates model generalization during development
+
+## Evaluation Protocol
+
+### Metrics
+- **Hit@K** (K=1, 5, 10): Whether GT item is in top-K predictions
+- **MRR**: Mean Reciprocal Rank
+- **NDCG@10**: Normalized Discounted Cumulative Gain
+
+### RQ4: Confidence-Rating Alignment
+Use `gt_rating` field to analyze correlation between model's confidence scores and actual user ratings.
+
+## Citation
+
+```bibtex
+@misc{kitrec2024,
+  title={KitREC: Knowledge-Instruction Transfer for Cross-Domain Recommendation},
+  author={KitREC Research Team},
+  year={2024},
+  note={Validation dataset for cross-domain recommendation evaluation}
+}
+```
+
+## License
+
+This dataset is released under the Apache 2.0 License.