The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Recommendation systems have been investigated and implemented in many aspects. Particularly, in case of collaborative filtering system, more important issue is how to manipulate the personalized recommendation results for better user understandability and satisfaction. Collaborative filtering system predicts items of interest for users based on predictive relationship discovered between the item and...
Recommender systems are widely used by companies that sell all or some of their products via the Internet. Furthermore, they are destined to take on an even more important role when their use is generalized as a Web 2.0 social service and is no longer only linked to e-commerce companies. The recommendations that a recommender system offers any given user are based on the preferences shown by a given...
Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have suffered from two fundamental problems: sparsity...
Collaborative filtering recommendation system is a widely used method of providing recommendations using explicit ratings on items from users, which provides personalized recommendations on products or services to customers. However, the current research on recommendation has paid little attention to the use of time-related data in the recommendation process and the study on collaborative filtering...
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.