  Recommender systems deployed in real-world applications can have inherent
exposure bias, which leads to the biased logged data plaguing the researchers.
A fundamental way to address this thorny problem is to collect users'
interactions on randomly expose items, i.e., the missing-at-random data. A few
works have asked certain users to rate or select randomly recommended items,
e.g., Yahoo!, Coat, and OpenBandit. However, these datasets are either too
small in size or lack key information, such as unique user ID or the features
of users/items. In this work, we present KuaiRand, an unbiased sequential
recommendation dataset containing millions of intervened interactions on
randomly exposed videos, collected from the video-sharing mobile App, Kuaishou.
Different from existing datasets, KuaiRand records 12 kinds of user feedback
signals (e.g., click, like, and view time) on randomly exposed videos inserted
in the recommendation feeds in two weeks. To facilitate model learning, we
further collect rich features of users and items as well as users' behavior
history. By releasing this dataset, we enable the research of advanced
debiasing large-scale recommendation scenarios for the first time. Also, with
its distinctive features, KuaiRand can support various other research
directions such as interactive recommendation, long sequential behavior
modeling, and multi-task learning. The dataset and its news will be available
at https://kuairand.com.
