Implicit-feedback Recommenders (ImplicitRec) leverage positive only user-item interactions, such as clicks, to learn personalized user preferences. Recommenders are often evaluated and compared offline using datasets collected from online platforms. These platforms are subject to popularity bias (i.e., popular items are more likely to be presented and interacted with), and therefore logged ground truth data are Missing-Not-At-Random (MNAR). As a result, the widely used Average-Over-All (AOA) evaluator is biased toward accurately recommending trendy items. In this paper, we (a) investigate evaluation bias of AOA and (b) develop an unbiased and practical offline evaluator for implicit MNAR datasets using the Inverse-Propensity-Scoring (IPS) technique. Through extensive experiments using four real-world datasets and four widely used algorithms, we show that (a) popularity bias is widely manifested in item presentation and interaction; (b) evaluation bias due to MNAR data pervasively exists in most cases where AOA is used to evaluate ImplicitRec; and (c) the unbiased estimator significantly reduces the AOA evaluation bias by more than 30% in the Yahoo! music dataset in terms of the Mean Absolute Error (MAE).

Nowadays, people spend more and more time with their smart-phones. They want to make good use of smartphones to achieve an efficient time management. However, many people fail to control the time spent on smartphones effectively and end up with a low task completion rate. We design a time management APP targeted to analyze the time management task completion. In this way, a clear picture of the reasons for a low task completion rate is drawn by us, which could be very useful to help people better manage their time and complete their tasks.

Summarizing large influence graphs is crucial for many graph visualization and mining tasks. Classical graph clustering and compression algorithms focus on summarizing the nodes by their structural-level or attribute-level similarities, but usually are not designed to characterize the flow-level pattern which is the centerpiece of influence graphs. On the other hand, the social influence analysis has been intensively studied, but little is done on the summarization problem without an explicit focus on social networks. Building on the recent study of the Influence Graph Summarization (IGS), this paper presents a new perspective of the underlying flow-based heuristic. It establishes a direct linkage between the optimal summarization and the classic eigenvector centrality of the graph nodes. Such a theoretic linkage has important implications on numerous aspects in the pursuit of a perfect influence graph summarization. In particular, it enables us to develop a suite of algorithms that can: 1) achieve a near-optimal IGS objective, 2) support dynamic summarizations balancing the IGS objective and the stability of transition in navigating the summarization, and 3) scale to million-node graphs with a near-linear computational complexity. Both quantitative experiments on real-world citation networks and the user studies on the task analysis experience demonstrate the effectiveness of the proposed summarization algorithms.