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With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR (Topic-Level Social Influence-based Microblogging Recommendation), which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods. [ABSTRACT FROM AUTHOR]

In recent years, millions of households have shifted from traditional TVs to smart TVs for viewing online videos on TV screens. In this article, we perform extensive analyses on a large‐scale online video viewing log on smart TVs. Because time influences almost every aspect of our lives, our aim is to understand temporal patterns of the online video viewing behavior of smart TV viewers at the crowd level. First, we measure the amount of time per hour spent in watching online videos on smart TV by each household on each day. By applying clustering techniques, we identify eight daily patterns whose peak hours occur in different segments of the day. The differences among households can be characterized by three types of temporal habits. We also uncover five periodic weekly patterns. There seems to be a circadian rhythm at the crow level. Further analysis confirms that there exists a holiday effect in the online video viewing behavior on smart TVs. Finally, we investigate the popularity variations of different video categories over the day. The obtained insights shed light on how we can partition a day to improve the performance of time‐aware video recommendations for smart TV viewers. [ABSTRACT FROM AUTHOR]