Amazon user item temporal bipartite rating network

Published: 11 February 2018| Version 2 | DOI: 10.17632/jy6rvth8w2.2
Khushnood Abbas


This bipartite network contains product ratings from the Amazon online shopping website. The rating scale ranges from 1 to 5, where 5 denotes the most positive rating. Nodes represent users and products, and edges represent individual ratings. More information about the network is provided here: Files: -- Metadata about the network -- The adjacency matrix of the network in space separated values format, with one edge per line The meaning of the columns in are: First column: ID of from node Second column: ID of to node Third column: edge weight Fourth column: timestamp of the edge Complete documentation about the file format can be found in the KONECT handbook, in the section File Formats, available at: All files are licensed under a Creative Commons Attribution-ShareAlike 2.0 Germany License. For more information concerning license visit Use the following References for citation: @MISC{konect:2016:amazon-ratings, title = {Amazon ratings network dataset -- {KONECT}}, month = oct, year = {2016}, url = {} } @inproceedings{konect:lim2010, author = {Lim, Ee-Peng and Nguyen, Viet-An and Jindal, Nitin and Liu, Bing and Lauw, Hady Wirawan}, title = {Detecting Product Review Spammers Using Rating Behaviors}, booktitle = {Proc. Int. Conf. on Information and Knowledge Management}, year = {2010}, pages = {939--948}, } @inproceedings{konect:jindal2008, author = {Jindal, Nitin and Liu, Bing}, title = {Opinion Spam and Analysis}, booktitle = {Proc. Int. Conf. on Web Search and Web Data Mining}, year = {2008}, pages = {219--230}, } @inproceedings{konect:mukherjee2012, author = {Mukherjee, Arjun and Liu, Bing and Glance, Natalie}, title = {Spotting Fake Reviewer Groups in Consumer Reviews}, booktitle = {Proc. Int. Conf. on World Wide Web}, year = {2012}, pages = {191--200}, } @inproceedings{konect, title = {{KONECT} -- {The} {Koblenz} {Network} {Collection}}, author = {Jérôme Kunegis}, year = {2013}, booktitle = {Proc. Int. Conf. on World Wide Web Companion}, pages = {1343--1350}, url = {}, url_presentation = {}, }


Steps to reproduce

Follow R code:)


University of Electronic Science and Technology of China - Qingshuihe Campus


Information Retrieval, Data Science, Machine Learning, Big Data, Recommendation System