Abstract: A method and arrangement for enabling creation of customized recommendations of items for persons (100) using a consumption device (102). Consumption samples are registered which refer to the consumption of items on the consumption device. Then, ratings of predefined features are determined from the consumption samples to form an aggregated profile (104b) of the persons. Individual taste profiles (104d) are then extracted from the aggregated profile by using an ICA algorithm (104c). Each taste profile reflects a distinct taste amongst the persons. The individual taste profiles can then be provided to a recommendation system (106) which is able to create customized recommendations of items (110).

Abstract: In one embodiment, a method of generating annotation tags (28) for a digital image (22) includes maintaining a library (16) of human-meaningful words or phrases organized as category entries (72) according to a number of defined image description categories (70), and receiving context metadata (20) associated with the capture of a given digital image (22). The method further includes selecting particular category entries (72-1, 72-2) as vocabulary metadata (24) for the digital image (22) by mapping the context metadata (20) into the library (16), and generating annotation tags (28) for the digital image (22) by logically combining the vocabulary metadata (24) according to a defined set of deductive logic rules (30) that are predicated on the defined image description categories (70). In another embodiment, a processing apparatus (12), such as a digital processor (18, 26) and supporting memory (14), etc., is configured to carry out the above method, or to carry out variations of the above method.

Abstract: In the embodiments of the present invention, a network node in an operator network is introduced. The network node is an analysis component configured to analyze the subscriber behavior based on the internet traffic data within the network. The network node is configured to provide a dynamic profile of the subscribers based on the current and past internet traffic. The dynamic profile may be used by other applications in the operator network or third parties. For example, a content provider can take a decision on what content to provide to a certain subscriber, based on dynamic subscriber profile information of this certain subscriber received from the network node according to the embodiments of the present invention. Another example is that an operator can use the dynamic subscriber profile when selecting commercial offers to his own subscribers e.g. when a subscriber has a new music mobile when visiting music sites.

Abstract: The invention relates to a server (100) for conveying a set of contact identification data to a user equipment (6) associated with a communication system (7), the server (100) comprising: a receiving unit (110) for receiving contact identification data from the user equipment (6); a data storage module (2) comprising subscriber specific data; and an analyzing module (3) arranged to: analyze received contact identification data, and provide the user equipment (6) with one or more suggested sets of contact identification data based on: the analysis, the subscriber specific data and global data of an analytics database (9). The invention also relates to corresponding methods, user equipment, computer programs and computer program products.

Abstract: A method and arrangement for processing data when training a data model involving multiple iterations of data records in a dataset (400c, 606) stored in a database (400, 600). Memory space (400d, 608) is allocated in the database for maintaining the data model during the training operation. The data records in the dataset are read (4:3) directly from the database for each iteration, and the data model is trained (4:4) inside the database by repeatedly applying the read data records in the training operation and updating (4:5) the data model. It is also checked (4:6) whether the updated data model has converged according to a predefined convergence condition. The data model is eventually saved (4:7) once the data model has converged.

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, Ru, 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.

Abstract: A method and apparatus are defined for organizing a plurality of digital photos. The method comprises the steps of identifying a group of digital photos, receiving a number defining how many clusters to be formed from the group, receiving profile information to be used for clustering the digital photos into the number of clusters, clustering the group of digital photos according to the profile information, and identifying representative digital photo(s) of the clusters from the clustered digital photos based on the profile information.

Abstract: Media tagging is significantly improved by fusing subjective, user-specific tags with collaborative, community based tags. Users share multimedia metadata tags in a network of users, to improve automatic tag generation for personal multimedia collections without compromising media privacy. In one method, a combined set of annotation tags is suggested to a user, for use in annotating a given media file. The combined set includes a first set drawn from a private, user-specific repository, and a second set drawn from a public, shared repository. In each case, determining which tags are suggested involves computing similarities between an attribute vector associated with the media file being tagged and attribute vectors associated with the tags. An attribute vector is a set of values representing given types of contextual metadata. The similarity determinations may be weighted according to user-specific and shared weights, and these weightings can be adapted to reflect user and community preferences.

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: Methods, devices, and computer-readable media described herein may provide a recommender system that may increase the serendipity associated with a recommendation. The recommender system omits obvious co-occurred items, rare items, and limits a number of co-occurred items associated with an item table. Local and global weighting values may be calculated to derive a co-occurrence weight. The co-occurrence weight may be compared to maximum and minimum threshold co-occurrence values to omit obvious and rare co-occurred items.

Abstract: A method, apparatus and system for generating recommendations of items to users. Ratings of items made by users are collected (1:1). User behaviour information is also collected (1:2). Then correlations in ratings are obtained (1:3) and similarities in user behaviour amongst the users are obtained (1:4). Thereafter, an item for recommendation to a user is identified (1:5), based on both the correlations in ratings and on the similarities in user behaviour amongst the users and the item is recommended to the user (1:6).

Abstract: Application servers and methods of operating the same are provided for generating a personalized recommendation of items to a first user. An updated rating value N(t) of an initial rating value N(0) is determined for each rated item based on an age of each rating. The updated rating value N(t) is based on a difference between the value of the initial rating N(0) and a neutral rating value R, and on a predetermined half-life of the ratings. The updated rating value N(t) converges towards the neutral rating value R with an increase in the age t for each rating.

Abstract: In one aspect, the invention provides a system that predicts the location of an entity, such as a friend of a user of a mobile device, and displays to the user this prediction information on a display screen of the mobile device. In some embodiments, the information may be displayed to the user in an augmented reality view (e.g., the prediction information may be displayed on top of an image as seen through a camera).

Abstract: In one embodiment, a method of generating annotation tags (28) for a digital image (22) includes maintaining a library (16) of human-meaningful words or phrases organized as category entries (72) according to a number of defined image description categories (70), and receiving context metadata (20) associated with the capture of a given digital image (22). The method further includes selecting particular category entries (72-1, 72-2) as vocabulary metadata (24) for the digital image (22) by mapping the context metadata (20) into the library (16), and generating annotation tags (28) for the digital image (22) by logically combining the vocabulary metadata (24) according to a defined set of deductive logic rules (30) that are predicated on the defined image description categories (70). In another embodiment, a processing apparatus (12), such as a digital processor (18, 26) and supporting memory (14), etc., is configured to carry out the above method, or to carry out variations of the above method.

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, Ru, 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.

Abstract: A method is provided for determining a correlation between a reference user and another user on the basis of two sets of ratings, where each rating is associated with a respective user. In response to a trigger,user ratings associated with the reference user and user ratings associated with the other user are collected, and all co-rated items of these two sets are correlated in the basis of an adjusted cosine correlation function which is weighted by a first and a second weighting function. The correlation is then stored and may be repeated for a plurality of users. The stored correlations may be used e.g. for ranking purposes.

Abstract: The present invention provides an improved recommender system. In some embodiments, the recommender system automatically builds a user profile for a user based solely on the action or actions the user takes with respect to certain documents and the contents of the documents. That is, the profile is based on implicit ratings. In addition to maintaining a user profile for each user, each document in a set of documents will have its own document profile, which is also referred to as a document vector. Advantageously, in some embodiments, the user profile will consist of the same attributes as the document vectors. This advantageous feature enables the recommender system to compare users and documents in order to generate recommendations.