  Recommender systems have played an increasingly important role in providing
users with tailored suggestions based on their preferences. However, the
conventional offline recommender systems cannot handle the ubiquitous data
stream well. To address this issue, Streaming Recommender Systems (SRSs) have
emerged in recent years, which incrementally train recommendation models on
newly received data for effective real-time recommendations. Focusing on new
data only benefits addressing concept drift, i.e., the changing user
preferences towards items. However, it impedes capturing long-term user
preferences. In addition, the commonly existing underload and overload problems
should be well tackled for higher accuracy of streaming recommendations. To
address these problems, we propose a Stratified and Time-aware Sampling based
Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy
of streaming recommendations. In STS-AEL, we first devise stratified and
time-aware sampling to extract representative data from both new data and
historical data to address concept drift while capturing long-term user
preferences. Also, incorporating the historical data benefits utilizing the
idle resources in the underload scenario more effectively. After that, we
propose adaptive ensemble learning to efficiently process the overloaded data
in parallel with multiple individual recommendation models, and then
effectively fuse the results of these models with a sequential adaptive
mechanism. Extensive experiments conducted on three real-world datasets
demonstrate that STS-AEL, in all the cases, significantly outperforms the
state-of-the-art SRSs.
