Update README.md

README.md

@@ -1,43 +1,143 @@
 ---
-dataset_info:
-  features:
-  - name: rating
-    dtype: float64
-  - name: title
-    dtype: string
-  - name: text
-    dtype: string
-  - name: images
-    list:
-    - name: attachment_type
-      dtype: string
-    - name: large_image_url
-      dtype: string
-    - name: medium_image_url
-      dtype: string
-    - name: small_image_url
-      dtype: string
-  - name: asin
-    dtype: string
-  - name: parent_asin
-    dtype: string
-  - name: user_id
-    dtype: string
-  - name: timestamp
-    dtype: int64
-  - name: helpful_vote
-    dtype: int64
-  - name: verified_purchase
-    dtype: bool
-  splits:
-  - name: full
-    num_bytes: 16256753028
-    num_examples: 29475453
-  download_size: 9687898981
-  dataset_size: 16256753028
-configs:
-- config_name: default
-  data_files:
-  - split: full
-    path: data/full-*
+language:
+- en
+tags:
+- recommendation
+- reviews
+size_categories:
+- 100M<n<1B
 ---
+# Amazon Reviews 2023 (Books Only)
+
+**This is a subset of Amazon Review 2023 dataset. Please visit [amazon-reviews-2023.github.io/](https://amazon-reviews-2023.github.io/) for more details, loading scripts, and preprocessed benchmark files.**
+
+**[April 18, 2024]** Update
+
+1. This dataset was created and pushed for the first time.
+
+---
+
+<!-- Provide a quick summary of the dataset. -->
+
+This is a large-scale **Amazon Reviews** dataset, collected in **2023** by [McAuley Lab](https://cseweb.ucsd.edu/~jmcauley/), and it includes rich features such as:
+1. **User Reviews** (*ratings*, *text*, *helpfulness votes*, etc.);
+2. **Item Metadata** (*descriptions*, *price*, *raw image*, etc.);
+
+## What's New?
+
+In the Amazon Reviews'23, we provide:
+
+1. **Larger Dataset:** We collected 571.54M reviews, 245.2% larger than the last version;
+2. **Newer Interactions:** Current interactions range from May. 1996 to Sep. 2023;
+3. **Richer Metadata:** More descriptive features in item metadata;
+4. **Fine-grained Timestamp:** Interaction timestamp at the second or finer level;
+5. **Cleaner Processing:** Cleaner item metadata than previous versions;
+6. **Standard Splitting:** Standard data splits to encourage RecSys benchmarking.
+
+## Basic Statistics
+
+> We define the <b>#R_Tokens</b> as the number of [tokens](https://pypi.org/project/tiktoken/) in user reviews and <b>#M_Tokens</b> as the number of [tokens](https://pypi.org/project/tiktoken/) if treating the dictionaries of item attributes as strings. We emphasize them as important statistics in the era of LLMs.
+
+> We count the number of items based on user reviews rather than item metadata files. Note that some items lack metadata.
+
+
+### Grouped by Category
+
+| Category                 | #User   | #Item   | #Rating   | #R_Token | #M_Token | Download                     |
+| ------------------------ | ------: | ------: | --------: | -------: | -------: | ------------------------------: |
+| Books | 10.3M | 4.4M | 29.5M | 2.9B | 3.7B | <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/review_categories/Books.jsonl.gz' download> review</a>, <a href='https://datarepo.eng.ucsd.edu/mcauley_group/data/amazon_2023/raw/meta_categories/meta_Books.jsonl.gz' download> meta </a> |
+meta </a> |
+> Check Pure ID files and corresponding data splitting strategies in <b>[Common Data Processing](https://amazon-reviews-2023.github.io/data_processing/index.html)</b> section.
+## Quick Start
+### Load User Reviews
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("cogsci13/Amazon-Reviews-2023-Books", "raw_review_Books", trust_remote_code=True)
+print(dataset["full"][0])
+```
+```json
+{'rating': 5.0,
+ 'title': 'Such a lovely scent but not overpowering.',
+ 'text': "This spray is really nice. It smells really good, goes on really fine, and does the trick. I will say it feels like you need a lot of it though to get the texture I want. I have a lot of hair, medium thickness. I am comparing to other brands with yucky chemicals so I'm gonna stick with this. Try it!",
+ 'images': [],
+ 'asin': 'B00YQ6X8EO',
+ 'parent_asin': 'B00YQ6X8EO',
+ 'user_id': 'AGKHLEW2SOWHNMFQIJGBECAF7INQ',
+ 'timestamp': 1588687728923,
+ 'helpful_vote': 0,
+ 'verified_purchase': True}
+```
+### Load Item Metadata
+```python
+dataset = load_dataset("cogsci13/Amazon-Reviews-2023-Books", "raw_meta_Books", split="full", trust_remote_code=True)
+print(dataset[0])
+```
+```json
+{'main_category': 'All Beauty',
+ 'title': 'Howard LC0008 Leather Conditioner, 8-Ounce (4-Pack)',
+ 'average_rating': 4.8,
+ 'rating_number': 10,
+ 'features': [],
+ 'description': [],
+ 'price': 'None',
+ 'images': {'hi_res': [None,
+   'https://m.media-amazon.com/images/I/71i77AuI9xL._SL1500_.jpg'],
+  'large': ['https://m.media-amazon.com/images/I/41qfjSfqNyL.jpg',
+   'https://m.media-amazon.com/images/I/41w2yznfuZL.jpg'],
+  'thumb': ['https://m.media-amazon.com/images/I/41qfjSfqNyL._SS40_.jpg',
+   'https://m.media-amazon.com/images/I/41w2yznfuZL._SS40_.jpg'],
+  'variant': ['MAIN', 'PT01']},
+ 'videos': {'title': [], 'url': [], 'user_id': []},
+ 'store': 'Howard Products',
+ 'categories': [],
+ 'details': '{"Package Dimensions": "7.1 x 5.5 x 3 inches; 2.38 Pounds", "UPC": "617390882781"}',
+ 'parent_asin': 'B01CUPMQZE',
+ 'bought_together': None,
+ 'subtitle': None,
+ 'author': None}
+```
+> Check data loading examples and Huggingface datasets APIs in <b>[Common Data Loading](https://amazon-reviews-2023.github.io/data_loading/index.html)</b> section.
+## Data Fields
+### For User Reviews
+| Field | Type |  Explanation |
+| ----- | ---- | ----------- |
+| rating | float | Rating of the product (from 1.0 to 5.0). |
+| title  | str | Title of the user review. |
+| text   | str | Text body of the user review. |
+| images | list | Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively. |
+| asin  | str | ID of the product. |
+| parent_asin | str | Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. <b>Please use parent ID to find product meta.</b> |
+| user_id | str | ID of the reviewer |
+| timestamp | int | Time of the review (unix time) |
+| verified_purchase | bool | User purchase verification |
+| helpful_vote | int | Helpful votes of the review |
+### For Item Metadata
+| Field | Type |  Explanation |
+| ----- | ---- | ----------- |
+| main_category | str | Main category (i.e., domain) of the product. |
+| title | str | Name of the product. |
+| average_rating | float | Rating of the product shown on the product page. |
+| rating_number | int | Number of ratings in the product. |
+| features | list | Bullet-point format features of the product. |
+| description | list | Description of the product. |
+| price  | float | Price in US dollars (at time of crawling). |
+| images | list |  Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image. |
+| videos | list | Videos of the product including title and url. |
+| store | str | Store name of the product. |
+| categories | list | Hierarchical categories of the product. |
+| details | dict | Product details, including materials, brand, sizes, etc. |
+| parent_asin | str | Parent ID of the product. |
+| bought_together | list | Recommended bundles from the websites. |
+## Citation
+```bibtex
+@article{hou2024bridging,
+  title={Bridging Language and Items for Retrieval and Recommendation},
+  author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian},
+  journal={arXiv preprint arXiv:2403.03952},
+  year={2024}
+}
+```
+## Contact Us
+- **Report Bugs**: To report bugs in the dataset, please file an issue on our [GitHub](https://github.com/hyp1231/AmazonReviews2023/issues/new).
+- **Others**: For research collaborations or other questions, please email **yphou AT ucsd.edu**.