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Ordinal regression has been widely used in applications, such as credit portfolio management, recommendation systems, and ecology, where the core task is to predict the labels on ordinal scales. Due to its learning efficiency, online ordinal regression using passive aggressive (PA) algorithms has gained a much attention for solving large-scale ranking problems. However, the PA method is sensitive to noise especially in the scenario of streaming data, where the ranking of data samples may change dramatically. In this paper, we propose a noise-resilient online learning algorithm using the Ramp loss function, called PA-RAMP, to improve the performance of PA method for noisy data streams. Also, we validate the order preservation of thresholds of the proposed algorithm. Experiments on real-world data sets demonstrate that the proposed noise-resilient online ordinal regression algorithm is more robust and efficient than state-of-the-art online ordinal regression algorithms. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), one of the most famous methods for building recommendation systems, recommends relevant items to users or predicting ratings of users' unknown items. Matrix factorization (MF) models are well-known model to deal with predicting the rating problem. However, the recommendation system based on matrix factorization is hard to keep up with the rapidly changing real-world data. When ratings on new users or new items come, the static model can not fit well on new data. As a consequence, if the current thing does not apply, the prediction accuracy will lose. In addition, it is a significant computation cost to rebuild the model on the whole data. To capture these changes, in this paper, we construct an online-and-offline Collaborative Filtering with a multi-method model to improve the traditional CF method, called Online SGD with Offline Knowledge (OSGDO for short). Besides, we propose a real-time incremental recommendation framework on Apache Flink, which is a scalable stream and batch data processing platform. Meanwhile, we implement our proposed method on our proposed framework. Our method proves to be good at online training when new observations arrive. And the results of experiments show that the dynamic training process we proposed is more efficient than rebuild the model on all the data. At the same time, our algorithm performs well in practice and can achieve impressive accuracy quickly when it is tested with the well-known data sets of MoviesLens and Netflix. [ABSTRACT FROM AUTHOR]

Intelligent exercise recommendation is a research focus in the field of online learning that can help learners quickly find exercises suitable for them from the exercise bank. However, exercise recommendation differs from product or film recommendation because of some special requirements. First, the recommended exercises must cover all knowledge points geared toward the learning objective of the learner. Second, the difficulty of exercises must match the knowledge level of the target learner. In response to the above requirements, this study proposes an exercise recommendation algorithm that integrates learning objective and assignment feedback. This algorithm considers not only the coverage of knowledge points but also the knowledge level of learners to help them find highly suitable exercises. According to this algorithm, the learning objective of the learner must be initially identified to obtain a course knowledge set that suits his/her learning objective. Second, the understanding of the learner about the knowledge set must be judged based on the assignment feedback. Third, suitable exercises are recommended based on the knowledge level of the learner and the course knowledge structure. The proposed algorithm is experimentally verified by using a real-world dataset and by comparing it with other algorithms. The experimental results show that the proposed algorithm significantly outperforms the other algorithms in both precision and recall. Based on these results, the proposed algorithm can achieve an excellent recommendation performance. [ABSTRACT FROM AUTHOR]