Amazon Product Rating Network
A Amazon.com (product reviews and ratings) dataset of 3,376,972 nodes and 5,743,258 edges, collected by J. McAuley & J. Leskovec via Amazon product pages. Each record contains directed: False, weighted: True, temporal: True. Large bipartite user-product rating network with timestamps and 1-5 star ratings — 5.8M edges across 2.1M users and 1.2M products.. Suitable for recommender systems, rating prediction, bipartite network analysis, e-commerce research.
Cite this dataset at
amazon-ratings.echo.directory
Analytics
The full graph has 3,376,972 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Computed over the full corpus.
Record
- Catalog key
amazon-ratings- Authors
- Lim, Ee-Peng; Nguyen, Viet-An; Jindal, Nitin; Liu, Bing; Lauw, Hady Wirawan
- Year
- 2010
- Source paper
- Detecting product review spammers using rating behaviors
- DOI / arXiv
- 10.1145/1871437.1871557
- License
- CC BY-SA 2.0 Germany (as stated by KONECT) (LicenseRef-CC-BY-SA-2.0-DE)
- Records
- 3,376,972
- Nodes
- 3,376,972
- Edges
- 5,743,258
- Nodes are
- user / product
- Edges are
- rating
- Attributes
- nodes have no attributes; edges have attributes
- Languages
- en
- Collected
- 2013 (2010s)
- Tasks
- Recommendation (ranking) inferred
From the graph: link prediction, community detection - Region
- Global (inferred: the platform is used worldwide)
Provenance
- Originally published at
- https://konect.cc/networks/amazon-ratings/ unverified
- Hosted copy
- Mirrored on echo.directory on 2026-10-02, 37.7 MB across 1 files. Checksums in MANIFEST.json.
Citations
Detecting product review spammers using rating behaviors
Ee-Peng Lim, V. Nguyen, N. Jindal, Bing Liu, Hady W. Lauw
Journal Article · International Conference on Information and Knowledge Management · 2010
Cited by
- A survey on opinion mining and sentiment analysis: Tasks, approaches and applications
Kumar Ravi, V. Ravi · Knowledge-Based Systems · 2015 · cited 1.3K× - What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM
Raffaele Filieri · 2015 · cited 999× - Collective Opinion Spam Detection: Bridging Review Networks and Metadata
Shebuti Rayana, L. Akoglu · Knowledge Discovery and Data Mining · 2015 · cited 781× - Book Review: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu
Jun Zhao, Kang Liu et al. · International Conference on Computational Logic · 2015 · cited 703× - A review of natural language processing techniques for opinion mining systems
Shiliang Sun, Chen Luo et al. · Information Fusion · 2017 · cited 524×
Show all 10
- Survey of review spam detection using machine learning techniques
M. Crawford, T. Khoshgoftaar et al. · Journal of Big Data · 2015 · cited 480× - REV2: Fraudulent User Prediction in Rating Platforms
Srijan Kumar, Bryan Hooi et al. · Web Search and Data Mining · 2018 · cited 470× - Misinformation in Social Media: Definition, Manipulation, and Detection
Liang Wu, F. Morstatter et al. · SKDD · 2019 · cited 424× - Exploiting Burstiness in Reviews for Review Spammer Detection
Ge-Li Fei, Arjun Mukherjee et al. · International Conference on Web and Social Media · 2021 · cited 374× - A systematic literature review: Opinion mining studies from mobile app store user reviews
Necmiye Genc-Nayebi, A. Abran · Journal of Systems and Software · 2017 · cited 344×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{Lim_2010,
series = {CIKM ’10},
title = {Detecting product review spammers using rating behaviors},
url = {http://dx.doi.org/10.1145/1871437.1871557},
doi = {10.1145/1871437.1871557},
booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management},
publisher = {ACM},
author = {Lim, Ee-Peng and Nguyen, Viet-An and Jindal, Nitin and Liu, Bing and Lauw, Hady Wirawan},
year = {2010},
month = Oct,
pages = {939–948},
collection = {CIKM ’10}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Lim, E.-P., Nguyen, V.-A., Jindal, N., Liu, B., & Lauw, H. W. (2010). Amazon Product Rating Network [Data set]. https://doi.org/10.1145/1871437.1871557
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Lim, Ee-Peng, et al. Amazon Product Rating Network. 2010, doi.org/10.1145/1871437.1871557.