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.
Neighborhood-based collaborative filtering (CF) methods are widely used in recommender systems because they are easy-to-implement and highly effective. One of the significant challenges of these methods is the ability to scale with the increasing amount of data since finding nearest neighbors requires a search over all of the data. Approximate nearest neighbor (ANN) methods eliminate this exhaustive...
Neighborhood-based collaborative filtering methods are widely used in recommender systems because of their easy-to-implement and effective nature. One important drawback of these methods is that they do not scale well with increasing amounts of data. In this work we applied the locality sensitive hashing technique for solving the scalability problem of neighborhood-based collaborative filtering. We...
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.