Datasets:
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user_id
int64 0
448k
| item_id
int64 0
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| ts
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| rating
float64 1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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1
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| 1
| 38
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1
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| 1
| 39
| 5
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1
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| 1
| 40
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1
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| 41
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1
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| 42
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1
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| 43
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1
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| 44
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1
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| 45
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1
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| 46
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1
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| 47
| 5
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1
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| 48
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1
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| 49
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1
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| 4
| 50
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1
| 1,847,001
| 4
| 51
| 5
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1
| 1,847,688
| 4
| 52
| 5
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1
| 1,847,688
| 4
| 53
| 5
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1
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| 4
| 54
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1
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| 4
| 55
| 5
|
1
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| 4
| 56
| 5
|
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in Data Studio
Amazon User Multi-Domain Dataset —— Amazon Users
(Amazon 用户多域数据集)
This dataset focuses on Cross-Domain Recommendation scenarios, aggregating user interaction data across multiple domains with Amazon Books serving as the primary domain.
📋 Data Filtering Criteria
The dataset is filtered to ensure high-quality cross-domain user behavior. Users must meet the following criteria to be included:
- Multi-Domain Activity: The user must be active in at least two domains (one of which must be Amazon Books).
- Interaction Thresholds:
- Primary Domain (Books): $\ge$ 5 interactions.
- Auxiliary Domain(s): $\ge$ 3 interactions.
📂 File Descriptions
| File Name | Description |
|---|---|
train/valid/test.csv |
Sequential User Interactions. The dataset is split using the leave-one-out strategy. |
meta_item.csv |
Item Metadata. Contains the title and category for all items. |
item_emb_qwen3_4B.npy |
Item Embeddings. Dense vector representations for all items, encoded using the Qwen/Qwen3-4B model. |
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