Constructing User Profiles for Collaborative Recommender System

Abstract. With the development of e-commerce and information ac-cess, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. In this paper1, clustering technique is applied in the collaborative recommender framework to con-sider semantic contents available from the user profiles. We also suggest methods to construct user profiles from rating information and attributes of items to accommodate user preferences. Further, we show that the cor-rect application of the semantic content information obtained from user profiles does enhance the effectiveness of collaborative recommendation.

Australian newspapers online: Four business models revisited

This article revisits four online news business models, first documented in 1997, to discuss current worldwide newspaper website trends and new research data on Australian newspaper websites. The data are from a survey of Australian newspapers and their websites, and show that the Australian experience mirrors international experience in terms of the growth of newspapers online and their lack of profitability. The survey shows that, while there is international evidence that providing news content online reduces offline newspaper subscriptions, a third of the newspapers studied registered circulation increases after setting up their websites. While there is international evidence that generating revenue through online advertising is difficult, for nearly half of the newspapers studied, overall advertising revenue increased after setting up their websites. The survey also found that, while newspaper publishers worldwide continue to rely mainly on the subscription and advertising business models to generate revenue online, there is evidence that Australian newspapers are forming online alliances with other media and non-media businesses to facilitate their online business activitie