
# 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]
size_categories:10K<n<100K language:en recommendation conversational recommendation sentiment analysis arxiv:1812.07617 region:us
{"language": ["en"], "size_categories": ["10K<n<100K"], "pretty_name": "ReDIAL", "config_names": ["SA", "rec", "autorec"], "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}], "tags": ["recommendation", "conversational recommendation", "sentiment analysis"]}
TAGS
#size_categories-10K<n<100K #language-English #recommendation #conversational recommendation #sentiment analysis #arxiv-1812.07617 #region-us 


# Dataset Card for ReDIAL

## Dataset Description

- Homepage: 
- Repository: 
 RecBot.
- Paper: 
- Leaderboard: 
- Point of Contact: 

### Dataset Summary

This is an adapted version of the original redial dataset, for supporting different tasks in our project RecBot.
The redial dataset provides over 10,000 conversations centered around movie recommendations. It was released in the paper "Towards Deep Conversational Recommendations" 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.


#### rec
An example of 'test' looks as follows.


#### autorec
An example of 'test' looks as follows.



### Data Fields
#### SA
- movieId: the movie's ID in the MovieLens 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



### Source Data

#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information







### Contributions


# Dataset Card for ReDIAL ## Dataset Description

- Homepage: 
- Repository: 
 RecBot.
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This is an adapted version of the original redial dataset, for supporting different tasks in our project RecBot.
The redial dataset provides over 10,000 conversations centered around movie recommendations. It was released in the paper "Towards Deep Conversational Recommendations" 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. #### rec
An example of 'test' looks as follows. #### autorec
An example of 'test' looks as follows. ### Data Fields #### SA
- movieId: the movie's ID in the MovieLens 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions
TAGS
#size_categories-10K<n<100K #language-English #recommendation #conversational recommendation #sentiment analysis #arxiv-1812.07617 #region-us 
 # Dataset Card for ReDIAL ## Dataset Description

- Homepage: 
- Repository: 
 RecBot.
- Paper: 
- Leaderboard: 
- Point of Contact: ### Dataset Summary

This is an adapted version of the original redial dataset, for supporting different tasks in our project RecBot.
The redial dataset provides over 10,000 conversations centered around movie recommendations. It was released in the paper "Towards Deep Conversational Recommendations" 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. #### rec
An example of 'test' looks as follows. #### autorec
An example of 'test' looks as follows. ### Data Fields #### SA
- movieId: the movie's ID in the MovieLens 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 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information ### Contributions