For years, machine learning has become the dominant approach to a variety of information retrieval tasks. The performance of machine learning algorithms heavily depends on their hyperparameters. It is hence critical to identity the optimal hyperparameter configuration when applying machine learning algorithms. Most of existing hyperparameter optimization methods assume a static relationship between hyperparameter configuration and algorithmic performance and are thus not suitable for many information retrieval applications with non-stationary environments such as e-commerce recommendation and online advertising. To address this limitation, we study online hyperparameter optimization, where the hyperparameter configuration is optimized on the fly. We formulate online hyperparameter optimization as a non-stationary continuum-armed bandits problem in which each arm corresponds to a hyperparameter configuration and the algorithmic performance is viewed as reward. For this problem, we develop principled methods with strong theoretical guarantees in terms of dynamic regret. The key idea is to adaptively discretize the continuous arm set and estimate the mean reward of each arm via weighted averaging. As a case application, we show how our methods can be applied to optimize the hyperparameter of vector-based candidate generation algorithm and empirically demonstrate the effectiveness and efficiency of our methods on public advertising dataset and online A/B testing. Furthermore, to the best of our knowledge, our methods are the first to achieve sub-linear dynamic regret bounds for continuum-armed bandits, which may be of independent interest.

The prediction of academic performance is one of the most important tasks in educational data mining, and has been widely studied in MOOCs and intelligent tutoring systems. Academic performance could be affected with factors like personality, skills, social environment, the use of library books and so on. However, it is still less investigated that how could the use of library books affect academic performance of college students and even leverage book-loan history for predicting academic performance. To this end, we propose a supervised content-aware matrix factorization for mutual reinforcement of academic performance prediction and library book recommendation. This model not only addresses the sparsity challenge by explainable dimension reduction techniques, but also promotes library book recommendation by recommending "right" books for students based on their performance levels and book meta information. Finally, we evaluate the proposed model on three years of the book-loan history and cumulative grade point average of 13,047 undergraduate students in one university. The results show that the proposed model outperforms the competing baselines on both tasks, and that academic performance is not only predictable from the book-loan history but also improves the recommendation of library books for students.