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 S tratified and T ime-aware S ampling based A daptive E nsemble L earning 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.

推荐系统在根据用户的偏好向用户提供量身定制的建议方面发挥了越来越重要的作用。但是，常规的离线推荐系统不能很好地处理普遍存在的数据流。为了解决此问题，近年来出现了流推荐系统（SRS），该系统逐步对新接收的数据进行推荐模型训练，以实现有效的实时推荐。关注新数据仅有益于解决概念漂移问题，即，用户对项目的偏好不断变化。但是，这阻碍了长期用户偏好的获取。另外，常见的欠载和过载应该更好地解决问题，以提高流媒体推荐的准确性。为了解决这些问题，我们提出了一个小号tratified和牛逼IME感知小号ampling基于一个daptive é nsemble大号收入框架，称为STS-AEL，用于提高流媒体推荐的准确性。在STS-AEL中，我们首先设计了分层且具有时间意识的采样，以从新数据和历史数据中提取代表性数据，以解决概念漂移，同时捕获长期的用户偏好。同样，在欠载情况下更有效地利用历史数据的优势来利用空闲资源。之后，我们提出了自适应集成学习，以与多个单个推荐模型并行高效地处理过载数据，然后通过顺序自适应机制有效地融合这些模型的结果。在三个真实的数据集上进行的广泛实验表明，在所有情况下，STS-AEL均明显优于最新的SRS。