---
license: apache-2.0
task_categories:
  - text-generation
language:
  - en
tags:
  - recommendation
  - cross-domain
  - evaluation
  - kitrec
  - test-data
size_categories:
  - 10K<n<100K
---

# KitREC Test Dataset - Set A

Evaluation test dataset for the KitREC (Knowledge-Instruction Transfer for Recommendation) cross-domain recommendation system.

## Dataset Description

This test dataset is designed for evaluating fine-tuned LLMs on cross-domain recommendation tasks across 10 different user types.

### Dataset Summary

| Attribute | Value |
|-----------|-------|
| **Candidate Set** | Set A (Hybrid (Hard negatives + Random)) |
| **Total Samples** | 30,000 |
| **Source Domain** | Books |
| **Target Domains** | Movies & TV, Music |
| **User Types** | 10 (5 per target domain) |
| **Rating Range** | 1.0 - 5.0 |
| **Mean Rating** | 4.259 |

### 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 |
|-----------|-------|------------|
| cold_start_2core_movies | 3,000 | 10.00% |
| cold_start_2core_music | 3,000 | 10.00% |
| cold_start_3core_movies | 3,000 | 10.00% |
| cold_start_3core_music | 3,000 | 10.00% |
| cold_start_4core_movies | 3,000 | 10.00% |
| cold_start_4core_music | 3,000 | 10.00% |
| overlapping_books_movies | 3,000 | 10.00% |
| overlapping_books_music | 3,000 | 10.00% |
| source_only_movies | 3,000 | 10.00% |
| source_only_music | 3,000 | 10.00% |


### 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 |
| `cold_start_2core_movies` | Movies cold-start users with 2 target interactions |
| `cold_start_2core_music` | Music cold-start users with 2 target interactions |
| `cold_start_3core_movies` | Movies cold-start users with 3 target interactions |
| `cold_start_3core_music` | Music cold-start users with 3 target interactions |
| `cold_start_4core_movies` | Movies cold-start users with 4 target interactions |
| `cold_start_4core_music` | Music cold-start users with 4 target interactions |
| `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 (10 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 |
|-------|---------|-------------|
| test | 30,000 | Evaluation test set |

## Usage

```python
from datasets import load_dataset

# Load test dataset
dataset = load_dataset("Younggooo/kitrec-test-seta")

# Access test data
test_data = dataset["test"]
print(f"Test samples: {len(test_data)}")

# Example: Filter by user type
overlapping_movies = test_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 test_data:
    user_type_metrics[sample["user_type"]].append(sample["gt_rating"])
```

## 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

### Stratified Analysis
Evaluate separately for each of the 10 user types to understand model performance across different scenarios.

### RQ4: Confidence-Rating Alignment
Use `gt_rating` field to analyze correlation between model's confidence scores and actual user ratings.

## Research Questions Addressed

| RQ | Question | Relevant Fields |
|----|----------|-----------------|
| RQ1 | KitREC structure effectiveness | All user types |
| RQ2 | Comparison with baselines | All metrics |
| RQ3 | Cold-start performance | cold_start_* user types |
| RQ4 | Confidence-rating alignment | gt_rating |

## Citation

```bibtex
@misc{kitrec2024,
  title={KitREC: Knowledge-Instruction Transfer for Cross-Domain Recommendation},
  author={KitREC Research Team},
  year={2024},
  note={Test dataset for cross-domain recommendation evaluation}
}
```

## License

This dataset is released under the Apache 2.0 License.