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Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of…
Social bookmarking is a system which allows users to share, organise, search and manage bookmarks of web resources. However, with the rapid growth in the production of online documents, people are facing the problem of information overload. Social bookmarking web sites offer a solution to this by providing push counts, which are counts of users’ recommendations of articles, and thus indicate the popularity and interest thereof. In this way, users can use the push counts to find popular and interesting articles. A measure of popularity-based solely on push counts, however, cannot be considered a true reflection of popularity. The paper aims to discuss these issues.
In this paper, the authors propose to derive the degree of popularity of an article by considering the reputation of the users who push the article. Moreover, the authors propose a novel personalised blog article recommendation approach which combines reputation-based group popularity with content-based filtering (CBF), for the recommendation of popular blog articles which satisfy users’ personal preferences.
The experimental results show that the proposed approach outperforms conventional CBF, item-based and user-based collaborative filtering approaches. The proposed approach considering reputation-based group popularity scores on neighbouring articles indeed can improve the recommendation quality of traditional CBF method.
The recommendation approach modifies CBF method by considering the target user's group preferences, to overcome the limitation of CBF which arises from the recommending only items similar to those the user has previously liked. Users with similar article preferences (profiles) may form a group of users with similar interests. A group's preferences may also reflect an individual's preferences. The reputation-based group preferences of the target user's group can be used to complement the target user's preferences.
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…
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.
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.
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.
Future studies might examine the effects of other recommendations or sorting methods to fit individual preferences in a more dynamic way.
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.
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.