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Web based commercial recommender systems (RS) can help users to make decisions about which product to purchase from the vast amount of products available on the Internet. Currently, many commercial recommender systems are developed for recommending frequently purchased products where a large amount of explicit ratings or purchase history data is available to predict user preferences. However, for...
With the rapid growth of e-commerce, there has been millions of products in a large ecommerce site where customer unable to effectively choose the products they are exposed to. To overcome the product overload problem, a variety of recommendation methods have been developed. Collaborative filtering (CF) is the most successful recommendation method. However, the CF method has two well-known limitations,...
A majority of web personalization research concentrates on customizing a single website. On the contrary, recommending web pages across websites is the focus of this study. We emphasize that eliciting user interests among different topics within a domain is an important concern in cross-website page recommendations. Enhancing Wikipedia's categorization system through heuristic information extraction,...
Like search engines, recommender systems have become a tool that cannot be ignored by websites with a large selection of products, music, news or simply webpages links. The performance of this kind of system depends on a large amount of information. At the same time, the amount of information on the Web is continuously growing, especially due to increased User Generated Content since the apparition...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.