  In today's business marketplace, many high-tech Internet enterprises
constantly explore innovative ways to provide optimal online user experiences
for gaining competitive advantages. The great needs of developing intelligent
interactive recommendation systems are indicated, which could sequentially
suggest users the most proper items by accurately predicting their preferences,
while receiving the up-to-date feedback to refine the recommendation results,
continuously. Multi-armed bandit algorithms, which have been widely applied
into various online systems, are quite capable of delivering such efficient
recommendation services. However, few existing bandit models are able to adapt
to new changes introduced by the modern recommender systems.
