Amazon dataset

doi:doi:10.57702/vm794mmk

Amazon dataset

The Amazon dataset is used to evaluate the performance of the proposed approach. It consists of 2000 users, 1500 items, 86690 reviews, 7219 number ratings, 3.6113 average number of rates per user, 0.2166 average number of rates per item and user ratings density is 0.0024.

BibTex:

@dataset{Pengpeng_Zhao_and_Soochow_University_and_Jian_Liu_and_Soochow_University_and_Yanchi_Liu_and_Rutgers_University_and_Victor_S_Sheng_and_University_of_Central_Arkansas_and_Fuzhen_Zhuang_and_Chinese_Academy_of_Sciences_and_Jiajie_Xu_and_Soochow_University_and_Xiaofang_Zhou_and_The_University_of_Queensland_and_Hui_Xiong_and_Rutgers_University_New_Jersey_2024,
    abstract = {The Amazon dataset is used to evaluate the performance of the proposed approach. It consists of 2000 users, 1500 items, 86690 reviews, 7219 number ratings, 3.6113 average number of rates per user, 0.2166 average number of rates per item and user ratings density is 0.0024.},
    author = {Pengpeng Zhao and Soochow University and Jian Liu and Soochow University and Yanchi Liu and Rutgers University and Victor S. Sheng and University of Central Arkansas and Fuzhen Zhuang and Chinese Academy of Sciences and Jiajie Xu and Soochow University and Xiaofang Zhou and The University of Queensland and Hui Xiong and Rutgers University New Jersey},
    doi = {10.57702/vm794mmk},
    institution = {No Organization},
    keyword = {'Aesthetic Preference', 'Amazon', 'Amazon dataset', 'Binary Classification', 'Cross-Domain Recommendation', 'Cross-domain', 'DSW-n-FCI', 'Dataset', 'E-commerce', 'Knowledge Transfer', 'Personality-based', 'Product Reviews', 'Product Search', 'Recommendation', 'Recommendation Systems', 'Recommender Systems', 'Reviews', 'Sentiment Analysis', 'Ternary Classification', 'Text Classification', 'opinion summarization', 'products', 'ratings', 'recommender systems', 'reviews', 'text classification', 'textual information'},
    month = {dec},
    publisher = {TIB},
    title = {Amazon dataset},
    url = {https://service.tib.eu/ldmservice/dataset/amazon-dataset},
    year = {2024}
}