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Online interactive recommender systems strive to promptly suggest to consumers appropriate items (e.g., movies, news articles) according to the current context including both the consumer and item content information. However, such context information is o en unavailable in practice for the recommendation, where only the users' interaction data on items can be utilized. Moreover, the lack of interaction records, especially for new users and items, worsens the performance of recommendation further. To address these issues, collaborative ltering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation se ings, by assuming independent items (i.e., arms). Nonetheless, the assumption rarely holds in reality, since the real-world items tend to be correlated with each other (e.g., two articles with similar topics).In this paper, we study online interactive collaborative ltering problems by considering the dependencies among items. We explicitly formulate the item dependencies as the clusters on arms, where the arms within a single cluster share the similar latent topics. In light of the topic modeling techniques, we come up with a generative model to generate the items from their underlying topics. Furthermore, an e cient online algorithm based on particle learning is developed for inferring both latent parameters and states of our model. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in the online interactive collaborating se ing. Empirical studies on two real-world applications, online recommendations of movies and news, demonstrate both the e ectiveness and e ciency of the proposed approach.

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

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