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}}