  Recommender systems play a vital role in modern online services, such as
Amazon and Taobao. Traditional personalized methods, which focus on user-item
(UI) relations, have been widely applied in industrial settings, owing to their
efficiency and effectiveness. Despite their success, we argue that these
approaches ignore local information hidden in similar users. To tackle this
problem, user-based methods exploit similar user relations to make
recommendations in a local perspective. Nevertheless, traditional user-based
methods, like userKNN and matrix factorization, are intractable to be deployed
in the real-time applications since such transductive models have to be
recomputed or retrained with any new interaction. To overcome this challenge,
we propose a framework called self-complementary collaborative filtering~(SCCF)
which can make recommendations with both global and local information in real
time. On the one hand, it utilizes UI relations and user neighborhood to
capture both global and local information. On the other hand, it can identify
similar users for each user in real time by inferring user representations on
the fly with an inductive model. The proposed framework can be seamlessly
incorporated into existing inductive UI approach and benefit from user
neighborhood with little additional computation. It is also the first attempt
to apply user-based methods in real-time settings. The effectiveness and
efficiency of SCCF are demonstrated through extensive offline experiments on
four public datasets, as well as a large scale online A/B test in Taobao.
