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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]

Abstract: Focused crawling is increasingly seen as a solution to address the scalability limitations of existing general-purpose search engines, by traversing the Web to only gather pages that are relevant to a specific topic. How to predict the relevance of the unvisited pages pointed to by candidate URLs in the crawling frontier to a given topic is a key issue in the design of focused crawlers. In this paper, we propose a novel approach based on multiple relevance prediction strategies to address this problem. For cross-language crawling, we first introduce a hierarchical taxonomy to describe topics in both English and Chinese. We then present a formal description of the relevance predicting process and discuss four strategies that make use of page contents, anchor texts, URL addresses and link types of Web pages, respectively, to evaluate the relevance more accurately, in which we propose a particular strategy using Chinese URL addresses to estimate the relevance of cross-language Web pages. Finally, we get a new focused crawling algorithm (FCMRPS, Focused Crawling based on Multiple Relevance Prediction Strategies) based on the combination of these strategies and Shark-Search, which is a classic focused crawling algorithm. Experiments show that the FCMRPS is more effective than the traditional algorithms, namely Breadth-First, Best-First and Shark-Search, in terms of precision and sum of information. [Copyright &y& Elsevier]