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---
dataset_info:
- config_name: SA
features:
- name: movieId
dtype: int32
- name: movieName
dtype: string
- name: messages
sequence: string
- name: senders
sequence: int32
- name: form
sequence: int32
splits:
- name: train
num_bytes: 33174059
num_examples: 41370
- name: validation
num_bytes: 8224594
num_examples: 10329
- name: test
num_bytes: 5151856
num_examples: 6952
download_size: 32552755
dataset_size: 46550509
- config_name: rec
features:
- name: movieIds
sequence: int32
- name: messages
sequence: string
- name: senders
sequence: int32
splits:
- name: train
num_bytes: 6064195
num_examples: 8004
- name: validation
num_bytes: 1511644
num_examples: 2002
- name: test
num_bytes: 937739
num_examples: 1342
download_size: 4812520
dataset_size: 8513578
- config_name: autorec
features:
- name: movieIds
sequence: int32
- name: ratings
sequence: float32
splits:
- name: train
num_bytes: 350688
num_examples: 7840
- name: validation
num_bytes: 87496
num_examples: 1966
- name: test
num_bytes: 58704
num_examples: 1321
download_size: 32552755
dataset_size: 496888
config_names:
- SA
- rec
- autorec
tags:
- recommendation
- conversational recommendation
- sentiment analysis
language:
- en
pretty_name: ReDIAL
size_categories:
- 10K<n<100K
---
# Dataset Card for ReDIAL
## Dataset Description
- **Homepage:**
- **Repository:**
[RecBot](https://github.com/McAuley-Lab/RecBot).
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is an adapted version of the [original redial dataset](https://huggingface.co/datasets/re_dial), for supporting different tasks in our project [RecBot](https://github.com/McAuley-Lab/RecBot).
The redial dataset provides over 10,000 conversations centered around movie recommendations. It was released in the paper ["Towards Deep Conversational Recommendations"](https://arxiv.org/abs/1812.07617) at NeurIPS 2018.
### Supported Tasks and Leaderboards
1. Sentiment Analysis: Use the SA config for sentiment analysis.
2. Recommendation: Use the autorec config for recommendation task.
3. Conversational recommendation: Use the rec config for conversational recommendation task.
### Languages
English
## Dataset Structure
### Data Instances
#### SA
An example of 'test' looks as follows.
```
{
"movieId": 111776,
"movieName": "Super Troopers",
"messages": [
"Hi I am looking for a movie like @111776",
"You should watch @151656",
"Is that a great one? I have never seen it. I have seen @192131\nI mean @134643",
"Yes @151656 is very funny and so is @94688",
"It sounds like I need to check them out",
"yes you will enjoy them",
"I appreciate your time. I will need to check those out. Are there any others you would recommend?",
"yes @101794",
"Thank you i will watch that too",
"and also @91481",
"Thanks for the suggestions.",
"you are welcome\nand also @124771",
"thanks goodbye"
],
"senders": [1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1],
"form": [0, 1, 1, 0, 1, 1]
}
```
#### rec
An example of 'test' looks as follows.
```
{
'movieIds': [111776, 91481, 151656, 134643, 192131, 124771, 94688, 101794],
'messages': ['Hi I am looking for a movie like @111776',
'You should watch @151656',
'Is that a great one? I have never seen it. I have seen @192131\nI mean @134643',
'Yes @151656 is very funny and so is @94688',
'It sounds like I need to check them out',
'yes you will enjoy them',
'I appreciate your time. I will need to check those out. Are there any others you would recommend?',
'yes @101794',
'Thank you i will watch that too',
'and also @91481',
'Thanks for the suggestions.',
'you are welcome\nand also @124771',
'thanks goodbye'],
'senders': [1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1]
}
```
#### autorec
An example of 'test' looks as follows.
```
{
"movieIds": [
111776,
151656,
134643,
192131,
94688
],
"ratings": [
1.0,
1.0,
1.0,
1.0,
1.0
]
}
```
### Data Fields
#### SA
- movieId: the movie's ID in the [MovieLens](https://grouplens.org/datasets/movielens/latest/) dataset.
- movieName: the movie's name.
- messages: a list of string. The conversation messages related to the movie. Note that one conversation can contain mutiple movies. The conversation messages are repeated for each movie as a sample.
- senders: a list of 1 or -1. It has the same length of messages. Each element indicates the message at the same index is from the initiatorWorker (with 1) or the respondentWorkerId (with -1).
- form: a list generated by: [init_q[movieId]["suggested"], init_q[movieId]["seen"], init_q[movieId]["liked"], resp_q[movieId]["suggested"], resp_q[movieId]["seen"], resp_q[movieId]["liked"]. init_q is the initiator questions in the conversation. resp_q is the respondent questions in the conversation.
#### rec
- movieIds: a list of movie ids in a conversation.
- messages: a list of string. see config SA for detail.
- senders: a list of 1 or -1. see config SA for detail.
#### autorec:
- movieIds: a list of movie ids in a conversation.
- ratings: a list of 0 or 1. It has the same length as movieIds. Each element indicates the inititator's "liked" value for the movie.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]