Abstract:
The invention provides an improved recommender system that includes a client device or service provider server, a trusted function handler module and a recommender module. The recommender system functions to protect the privacy of user rating information maintained by the node (i.e., client device/server) by having the node transform the user rating information using a specific function selected by the function handler and then provide the transformed user rating information to the recommender module. In this way, privacy of the user rating information is maintained because the original user rating information will be unknown to a recommender module.
Abstract:
A method of providing content (130) associated with a weight-value, the content (130) previously provided to a current computer associated with a current user (116) that is represented by a first node (106) in a social network (101). The method comprises the steps of: i) enabling the current computer (216) to display the content (130), in dependence of the weight-value, ii) obtaining an input of the current user (116), iii) updating the weight-value of the content (130), in dependence of the input of the current user (116), iv) determining a receiving computer associated with a second node (107) in the social network (101), and v) providing the content (130) to the receiving computer. Corresponding computers, computer program and computer readable medium are also described.
Abstract:
A method of estimating a default rating of a rated dataset is provided, where the dataset comprises at least one series of ratings associated with at least one user and each series comprise ratings associated with at least two items. For a reference user and an item for which a rated value is missing the item' s average rating, ir, the reference users average rating, R u , and the datasets average rating, dr, is collected. A Poisson distribution of the reference users rating is then generated on the basis of the reference users average rating. A random Poisson rating, ur, is calculated on the basis of the Poisson distribution, and a default rating, r, is estimated by weighting the random Poisson rating on the basis of the items average rating, the users average rating and the datasets average rating.