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--- |
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task_categories: |
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- text-classification |
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- feature-extraction |
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- sentence-similarity |
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- text2text-generation |
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language: |
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- en |
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tags: |
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- e-commerce |
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- products |
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- amazon |
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size_categories: |
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- 100K<n<1M |
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--- |
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# Dataset Card for Amazon Products 2023 |
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## Dataset Summary |
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This dataset contains product metadata from Amazon, filtered to include only products that became available in 2023. The dataset is intended for use in semantic search applications and includes a variety of product categories. |
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- **Number of Rows:** 117,243 |
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- **Number of Columns:** 15 |
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## Data Source |
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The data is sourced from [Amazon Reviews 2023](https://amazon-reviews-2023.github.io/). It includes product information across multiple categories, with embeddings created using the `text-embedding-3-small` model. |
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## Dataset Structure |
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### Columns |
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- **parent_asin (str):** Unique identifier for the product. |
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- **date_first_available (datetime64[ns]):** The date when the product first became available. |
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- **title (str):** Title of the product. |
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- **description (str):** Description of the product. |
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- **filename (str):** Filename associated with the product metadata. |
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- **main_category (str):** Main category of the product. |
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- **categories (List[str]):** Subcategories of the product. |
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- **store (str):** Store information for the product. |
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- **average_rating (float64):** Average rating of the product. |
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- **rating_number (float64):** Number of ratings for the product. |
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- **price (float64):** Price of the product. |
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- **features (List[str]):** Features of the product. |
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- **details (str):** Additional details of the product. The string is JSON serializable. |
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- **embeddings (List[float64]):** Embeddings generated for the product using `text-embedding-3-small` model. |
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- **image (str):** URL of the product image. |
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### Missing Values |
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- **main_category:** 24,805 missing values |
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- **store:** 253 missing values |
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- **rating_number:** 6 missing values |
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- **price:** 35,869 missing values |
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### Sample Data |
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```json |
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[ |
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{ |
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"parent_asin": "B000044U2O", |
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"date_first_available": "2023-04-29T00:00:00", |
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"title": "Anomie & Bonhomie", |
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"description": "Amazon.com Fans of Scritti Politti's synth-pop-funk masterpiece Cupid & Psyche 85 may be shocked by how far afield Scritti mastermind Green Gartside has gone since then. Anomie & Bonhomie, his return to recording after a decadelong absence, ranges from guest shots by rappers and funksters such as Mos Def and Me'Shell Ndegeocello to Foo Fighters tributes. Gartside's trademark breathy vocals and spot-on melodicism do find their places here, but are often forced to make way for other influences. Neither a total success nor a total failure, Anomie does display a spark that makes one hope that Gartside doesn't wait so long to record again. --Rickey Wright", |
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"filename": "meta_Digital_Music", |
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"main_category": "Digital Music", |
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"categories": [], |
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"store": "Scritti Politti Format: Audio CD", |
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"average_rating": 4.2, |
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"rating_number": 56.0, |
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"price": null, |
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"features": [], |
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"details": "{'Date First Available': 'April 29, 2023'}", |
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"embeddings": [], |
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"image": "https://m.media-amazon.com/images/I/41T618NE88L.jpg" |
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}, |
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... |
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] |
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``` |
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### Usage |
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This dataset can be used for various applications, including: |
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- Semantic Search: Utilizing the embeddings to find similar products based on textual descriptions. |
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- Product Recommendation: Enhancing recommendation systems with detailed product metadata. |
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### Citation |
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```bibtex |
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@article{hou2024bridging, |
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title={Bridging Language and Items for Retrieval and Recommendation}, |
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author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian}, |
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journal={arXiv preprint arXiv:2403.03952}, |
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year={2024} |
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} |
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``` |
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### Contact |
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For questions or issues regarding the dataset, please contact [Amazon Reviews 2023](https://amazon-reviews-2023.github.io/). |
