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---
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}} |