README.md

---
dataset_info:
  features:
  - name: video_id
    dtype: int64
  - name: recall_score
    dtype: float64
  - name: youtube_id
    dtype: string
  - name: ad_details
    struct:
    - name: Audio
      dtype: string
    - name: Brand
      dtype: string
    - name: Duration
      dtype: string
    - name: Orientation
      dtype: string
    - name: Pace
      dtype: string
    - name: Scenes
      list:
      - name: Colors
        dtype: string
      - name: Description
        dtype: string
      - name: Emotions
        dtype: string
      - name: Number
        dtype: string
      - name: Photography Style
        dtype: string
      - name: Tags
        dtype: string
      - name: Text Shown
        dtype: string
      - name: Tone
        dtype: string
      - name: Visual Complexity
        dtype: string
    - name: Title
      dtype: string
  splits:
  - name: train
    num_bytes: 5490622.457169034
    num_examples: 1964
  - name: test
    num_bytes: 612243.5428309665
    num_examples: 219
  download_size: 2551503
  dataset_size: 6102866
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: mit
pretty_name: Long Term Memorability of Advertisements (LAMBDA)
task_categories:
- text-classification
- text-generation
- question-answering
tags:
- memorability
- long-term-memorability
- advertisement memorability
---


## Dataset Description

- **Website:** https://behavior-in-the-wild.github.io/memorability
- **Paper:** https://arxiv.org/abs/2309.00378

### Dataset Summary
LAMDBA is a long term ad memorability dataset, featuring data from 1749 participants and 2205 ads across 276 brands.

## Dataset Structure

```python
from datasets import load_dataset
ds = load_dataset("behavior-in-the-wild/LAMBDA")
ds

DatasetDict({
    train: Dataset({
        features: ['video_id', 'recall_score', 'youtube_id', 'ad_details'],
        num_rows: 1964
    })
    test: Dataset({
        features: ['video_id', 'recall_score', 'youtube_id', 'ad_details'],
        num_rows: 219
    })
})
```

### Data Fields

- `video_id`: identifier for the data sample
- `recall_score`: memorability score for the video between 0 to 1
- `youtube_id`: youtube id for the video
- `ad_details`: scene by scene features for each video

## Citation
@misc{s2024longtermadmemorabilityunderstanding,
            title={Long-Term Ad Memorability: Understanding and Generating Memorable Ads}, 
            author={Harini S I au2 and Somesh Singh and Yaman K Singla and Aanisha Bhattacharyya and Veeky Baths and Changyou Chen and Rajiv Ratn Shah and Balaji Krishnamurthy},
            year={2024},
            eprint={2309.00378},
            archivePrefix={arXiv},
            primaryClass={cs.CL},
            url={https://arxiv.org/abs/2309.00378}}