  Multi-armed bandits (MAB) provide a principled online learning approach to
attain the balance between exploration and exploitation. Due to the superior
performance and low feedback learning without the learning to act in multiple
situations, Multi-armed Bandits drawing widespread attention in applications
ranging such as recommender systems. Likewise, within the recommender system,
collaborative filtering (CF) is arguably the earliest and most influential
method in the recommender system. Crucially, new users and an ever-changing
pool of recommended items are the challenges that recommender systems need to
address. For collaborative filtering, the classical method is training the
model offline, then perform the online testing, but this approach can no longer
handle the dynamic changes in user preferences which is the so-called cold
start. So how to effectively recommend items to users in the absence of
effective information? To address the aforementioned problems, a multi-armed
bandit based collaborative filtering recommender system has been proposed,
named BanditMF. BanditMF is designed to address two challenges in the
multi-armed bandits algorithm and collaborative filtering: (1) how to solve the
cold start problem for collaborative filtering under the condition of scarcity
of valid information, (2) how to solve the sub-optimal problem of bandit
algorithms in strong social relations domains caused by independently
estimating unknown parameters associated with each user and ignoring
correlations between users.
