Abstract:
Method, apparatus, system, computer program product and computer readable medium are disclosed for recommending content to a plurality of users. Each of the users is associated with a user score. The method comprises determining a recommending score for an item of content at least partly based on a user's promotion of the item and the user score of the promoting user; recommending the item according to its recommending score; and adjusting the user score of the promoting user based on other users' feedback with respect to the item promoted by said user.
Abstract:
Method, apparatus, system, computer program product and computer readable medium are disclosed for recommending content to a plurality of users. Each of the users is associated with a user score. The method comprises determining a recommending score for an item of content at least partly based on a user's promotion of the item and the user score of the promoting user; recommending the item according to its recommending score; and adjusting the user score of the promoting user based on other users' feedback with respect to the item promoted by said user.
Abstract:
An approach is provided for a method for intelligent and personalized web information discovery and user interface. A method can comprise building a hierarchical, tree-structured topic model involving data intelligence and human intelligence, that comprises one or more nodes which have respective topics and are configured to map the respective topics to display spaces of a user interface. The method can further comprise collecting web contents matched with the respective topics causing to render information of the collected web contents in the display spaces mapped to the respective topics.
Abstract:
Method, apparatus, system, computer program product and computer readable medium are disclosed for recommending content to a plurality of users. Each of the users is associated with a user score. The method comprises determining a recommending score for an item of content at least partly based on a user's promotion of the item and the user score of the promoting user; recommending the item according to its recommending score; and adjusting the user score of the promoting user based on other users' feedback with respect to the item promoted by said user.
Abstract:
A method, apparatus and computer-readable storage medium for determining one or more recommendations by applying efficient adaptive matrix factorization are disclosed. The method comprises causing, at least in part, an iterative performing of the following steps: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period.
Abstract:
A method and apparatus for enriching social media to improve personalized user experience are provide, the method comprising: receiving highlights and/or annotations in at least one electronic document made by at least one user; extracting keywords from the respective at least one electronic document with the highlights and/or annotations as tags of the respective at least one electronic document; and using the keywords as tags of the respective at least one electronic document to provide personalized contents from the at least one electronic document to a user. Thus, by having high quality/relevant tags from a plurality of users for a given document, we may better profile the document. Similarly, by having high quality and insightful tags that a user has given to a plurality of documents, we may better profile the user's interest and behavior. By having better document and user profiling, we may better recommend the right documents to right users. In addition, we may offer more interesting UI features to improve user experience and engagement.