In educational practice, teachers often need to manually assemble an exercise collection as a class quiz or a homework assignment. A well-assembled exercise collection needs to have the proper difficulty index and discrimination index so that it can better develop students' abilities. In this paper, we propose an exercise collection auto-assembling framework, in which a teacher provides the target values of difficulty and discrimination indices and a qualified exercise collection is automatically assembled. The framework consists of two stages. At the answer prediction stage, a knowledge tracing model is utilized to predict the students' answers to unseen exercises based on their history interaction records. In addition, to better represent the exercises in the model, we propose exercise embeddings and design a pre-training approach. At the collection assembling stage, we propose a deep reinforcement learning model to assemble the required exercise collection effectively. Since the knowledge tracing model in the first stage has different confidences in the predicted answers, it is also taken into account in the objective. Experimental results show the effectiveness and efficiency of the proposed framework.

The rapid development of social networks has resulted in a proliferation of user-generated content (UGC), which can benefit many applications. In this paper, we study the problem of identifying a user’s locations from microblogs, to facilitate effective location-based advertisement and recommendation. Since the location information in a microblog is incomplete, we cannot get an accurate location from a local microblog. As such, we propose a global location identification method, GLITTER. GLITTER combines multiple microblogs of a user and utilizes them to identify the user’s locations. GLITTER not only improves the quality of identifying a user’s location but also supplements the location of a microblog so as to obtain an accurate location of a microblog. To facilitate location identification, GLITTER organizes points of interest (POIs) into a tree structure where leaf nodes are POIs and non-leaf nodes are segments of POIs, e.g., countries, cities, and streets. Using the tree structure, GLITTER first extracts candidate locations from each microblog of a user which correspond to some tree nodes. Then GLITTER aggregates these candidate locations and identifies top-k locations of the user. Using the identified top-k user locations, GLITTER refines the candidate locations and computes top-k locations of each microblog. To achieve high recall, we enable fuzzy matching between locations and microblogs. We propose an incremental algorithm to support dynamic updates of microblogs. We also study how to identify users’ trajectories based on the extracted locations. We propose an effective algorithm to extract high-quality trajectories. Experimental results on real-world datasets show that our method achieves high quality and good performance, and scales well.