Purpose – The purpose of this paper is to evaluate the performance of the multi-source book review system (MBRS). MBRS was designed to reduce information overload using the internet and to accommodate different learner preferences. Design/methodology/approach – The authors experimentally compared MBRS with the Google search engine. MBRS first gathers reviews from online sources, such as bookstores and blogs. It reduces information overload through an advanced filtering and sorting algorithm and by providing a uniform user interface. MBRS accommodates different learning styles through various sort options and through adding video-mediated reviews. Findings – Results indicate that, compared with Google, MBRS: reduces the information overload associated with searching for online book reviews; increases users finding satisfactory book reviews; and allows users to find reviews more quickly. In addition, more than half of the participants found video-mediated book reviews more appealing than traditional text-based reviews. Research limitations/implications – Future studies might examine the effects of other recommendations or sorting methods to fit individual preferences in a more dynamic way. Practical implications – This study assisted readers with a preference for visual information in locating reviews of personal interest in less time and with finding reviews more aligned with their individual learning preferences. Originality/value – This study documents an innovative web site featuring video-mediated book reviews and other mechanisms to accommodate individual preferences. Search engine designers could integrate book reviews with different media types to reduce cognitive load allowing readers to focus attention on the reading task. Internet booksellers or library staff may use this as an effective means to enhance reading motivation.

The new generation of web-based communities, Web2.0, represents an innovative spirit in sharing and managing contents. Social bookmarking is a portal for users to share, organize, search, and manage bookmarks of web resources. However, with the rapid growth of web documents that are produced every day, people are facing the problem of information overload. The Social bookmarking web site provides the push (user recommendation) counts of articles indicating the recommended popularity degrees of articles. In this paper, we propose to derive the popularity degree of an article by considering the reputation of users that push the article. Moreover, we propose a personalized blog article recommendation approach, which combines the reputation-based popularity with content based filtering, to recommend popular blog articles to users that satisfy their personal preferences. Our experimental results show that the proposed approach outperforms conventional approaches.

PM2.5 is invisible to the eye, but it is a threat to human health. In order to monitor the small-scale environment status, Taiwan's Environmental Protection Administration (EPA) have deployed more than 2,500 environmental IoT Sensor in 13 cities ( Figure 1) to monitor air quality. These sensors are mainly deployed in industrial areas, gather minute-byminute environmental data on PM2.5, temperature, and humid, etc., try to evaluate local pollution hot zone in each area.Mainstream air quality sensing relies on high-precision large-scale stations, but it is difficult to collect local air quality information. In fact, Factories, temples or local air pollution emission behaviour can strongly affect the air quality, and some emissions have already exceeded acceptable limits. However, emissions disperse quickly with the wind in all directions, cause the environment changes greatly in a very short time. As a result, it is difficult to determine the source of emissions, which is a serious problem for environmental protection administration.