Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Datasets and Benchmarks Track
Authors: Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun YU, Bo Hu, Zang Li, Yu Xu, Xiaohu Qie

Existing benchmark datasets for recommender systems (RS)  either are created  at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. To be specific, Tenrec has the following five characteristics: (1) it is large-scale, containing around 5 million users and 140 million interactions; (2) it has not only positive user feedback, but also true  negative feedback (vs. one-class recommendation); (3) it contains overlapped users and items across four different scenarios; (4) it contains various types of  user positive feedback, in forms of clicking, liking, sharing, and following, etc; (5) it contains additional features beyond the user IDs and item IDs. We verify Tenrec on ten diverse  recommendation  tasks by running several classical baseline models per task. Tenrec has the potential to become a  useful benchmark dataset for a majority of popular recommendation tasks.  Our source codes and datasets will be included  in supplementary materials.