metadata

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.
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

Sentiment Analysis: Use the SA config for sentiment analysis.
Recommendation: Use the autorec config for recommendation task.
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 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

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

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