File size: 6,343 Bytes
8929825
 
 
 
79d3904
8929825
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0a98b99
8929825
 
 
 
 
0a98b99
8929825
 
 
 
 
 
54864fe
 
8929825
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79d3904
8929825
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17afaa0
 
f26a522
17afaa0
 
 
 
8929825
17afaa0
 
 
f26a522
8929825
17afaa0
 
 
8929825
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
---
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]