  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 clicks, likes,
shares, and follows, 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.
