  Data plays a vital role in machine learning studies. In the research of
recommendation, both user behaviors and side information are helpful to model
users. So, large-scale real scenario datasets with abundant user behaviors will
contribute a lot. However, it is not easy to get such datasets as most of them
are only hold and protected by companies. In this paper, a new large-scale
dataset collected from a knowledge-sharing platform is presented, which is
composed of around 100M interactions collected within 10 days, 798K users, 165K
questions, 554K answers, 240K authors, 70K topics, and more than 501K user
query keywords. There are also descriptions of users, answers, questions,
authors, and topics, which are anonymous. Note that each user's latest query
keywords have not been included in previous open datasets, which reveal users'
explicit information needs.
  We characterize the dataset and demonstrate its potential applications for
recommendation study. Multiple experiments show the dataset can be used to
evaluate algorithms in general top-N recommendation, sequential recommendation,
and context-aware recommendation. This dataset can also be used to integrate
search and recommendation and recommendation with negative feedback. Besides,
tasks beyond recommendation, such as user gender prediction, most valuable
answerer identification, and high-quality answer recognition, can also use this
dataset. To the best of our knowledge, this is the largest real-world
interaction dataset for personalized recommendation.
