大众点评数据集 (TKDE’19, DMKD’19, RecSys’15, SIGIR’14)

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1. Dianping_SequentialRec (2019)

This is the anonymized Dianping dataset used in our TKDE’19 paper for evaluating sequential recommendation task. It contains 616,331 users, 10,979 restaurants and 3,868,306 actions from April 2003 to November 2013 in Shanghai, China. There are 247 restaurant categories. For privacy issue, we do not include the user information and restaurant attributes which can be used to identify a real person. Please see README file for details of data format.   [download]

2. Dianping_SocialRec (2015)

This is part of the anonymized Dianping dataset used in our RecSys’15 paper for evaluating the quality of social recommender systems. It contains 147,918 users, 11,123 restaurants and 2,149,675 ratings from April 2003 to November 2013 in Shanghai, China. For the social friend network, there are a total of 629,618 claimed social relationships (undirected edge). For privacy issue, we do not include the user information and restaurant attributes which can be used to identify a real person. Please see README file for details of data format.   [download]

3. Dianping_SocialRec (2014)

This is the anonymized Dianping dataset used in our SIGIR’14 and DMKD’19 papers for evaluating the quality of social recommender systems. It contains 11,352 users, 10,657 restaurants and 501,472 ratings from April 2003 to November 2013 in Shanghai, China. For the social friend network, there are a total of 280,041 claimed social relationships (directed edge). We cleaned those edges that point to users who do not have ratings in this dataset, while we used these edges in our experiments. Thus the edge number is smaller than we reported in the paper. Please see README file for details of data format.   [download]

Citation

如果你在科研中使用大众点评数据集,请引用我们的论文 [bib]:

[1] Hui Li, Ye Liu, Nikos Mamoulis, and David S. Rosenblum. Translation-Based Sequential Recommendation for Complex Users on Sparse Data. In TKDE, 2019.
[2] Hui Li, Yu Liu, Yuqiu Qian, Nikos Mamoulis, Wenting Tu, and David W. Cheung. HHMF: Hidden Hierarchical Matrix Factorization for Recommender Systems. In DMKD, 2019.
[3] Hui Li, Dingming Wu, Wenbin Tang, and Nikos Mamoulis. Overlapping community regularization for rating prediction in social recommender systems. In RecSys, pages 27--34, 2015.
[4] Hui Li, Dingming Wu, and Nikos Mamoulis. A revisit to social network-based recommender systems. In SIGIR, pages 1239--1242, 2014.
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