In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.

One or more computing devices, systems, and/or methods for generating dynamic content item recommendations are provided. Content item information, extracted from message data, is aggregated to calculate popularity and attributes of content items. The content items are ranked based upon the popularity and attributes to generate a ranked list of content items. Exploration traffic is served utilizing a set of eligible content items selected from the ranked list of content items. An eligible content item is promoted for participation in auctions for serving non-exploration traffic based upon the eligible content item being served a threshold number of times.

A method for recommending household goods and clothing information to a first user of household goods and clothing, and an apparatus thereof. The method includes: transmitting a request for recommended household goods and clothing for the first user, to be displayed on the first user's display; receiving the purchase history of one or more of household goods and clothing by a plurality of users of the one or more of the household goods and clothing; extracting a second user, from among the plurality of users, who has purchased a household good or clothing with a design or function similar to a household good or clothing purchased by the first user; transmitting a recommendation for the first user of a household good or clothing purchased by the second user; and displaying on the display of the first user, the recommended household good or clothing purchased by the second user.

Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Systems and methods for optimizing base pricing of products within a physical retailer are provided. Such systems and methods include first collecting transaction logs for products in a set of physical retail spaces. These logs are validated, adjusted and elasticities between the products are computed. The adjustment may be responsive to the day, by retailer and by a host of external factors (e.g., weather). The adjustment may also include a normalization and filtering out of inaccurate log data. Elasticity is calculated by machine learning models. A set of constraints are then received and used, along with the elasticities to compute the optimal prices for deployment in retailers for further testing.

The present invention relates to systems and methods for matching and collecting user data and/or user device data within a current Internet access session of a user for use by user notification systems that generate, distribute and display informational messages over the Internet. The system comprises a source data reception unit configured to receive a source IP address and a source user device port matched with the translated IP address and with the translated port of the operator or the provider from the NAT service, and a data matching unit configured to match user data and/or user device data from all available sources, including but not limited to operator or provider databases, using the received source IP address and the received user device port. Advantageously, the invention provides delivery of informational messages based on collected/matched user data and/or user device data provided according to the present system to the maximum number of real identified users.

Methods and system for providing information selected from a large set of digital content to at least one user. One such method comprises receiving user context information associated with the at least one user and identifying or generating, using at least one processor executing stored program instructions, a first concept in a semantic network, the first concept representing at least a portion of the user context information. The method further comprises obtaining at least one concept, including a second concept, semantically relevant to the first concept at least in part, by synthesizing the second concept based on the first concept and at least one other concept in the semantic network; and providing information to the at least one user, wherein the information is selected by using the first concept and the at least one obtained concept semantically relevant to the first concept, wherein the first concept in a semantic network is represented by a data structure storing any data associated with a node in the semantic network.

Techniques are disclosed utilizing cognitive computing to improve banking experiences. A user's financial account(s) may be monitored to predict when a surplus of funds is unnecessarily present and for how long this will likely be the case. Once this is determined, techniques include automatically drafting funds from the account to another account having a higher interest rate where the funds may accrue more interest. The techniques also include predicting when an overdraft may occur and taking appropriate action when such a prediction is made. Predictions may be based upon different weighted inputs used in accordance with a predictive modeling system, which may attempt to predict for a particular user, location, and retailer, whether the user will spend an anticipated amount in excess of the user's current balance. If so, passive (e.g., notifications) and active (e.g., transferring cover funds) actions may be performed.

This invention relates in general to the field of cell-therapy treatments and more particularly, but not by way of limitation, to systems and methods for administering personalized cell-therapy treatments intravenously. In various embodiments, the system may calculate an aspiration volume needed for centrifugation to achieve a concentrated target threshold dose of 2×10.sup.6 platelets/μL for a particular cell therapy using various factors such as, for example, information about a patient and the efficiency of the concentration process.

A method includes receiving interaction data indicating a performance of a specified action by a user of a user device, identifying a last-in-time action associated with the user and an initiation action associated with the user and the specified action, generating, based on the identified last-in-time and initiation actions, a first attribution associated with the identified last-in-time action and the specified action and a second, additional attribution associated with the identified initiation action and the specified action, propagating, to two or more different models, the first attribution and the second, additional attribution, and generating, based on the first attribution and the second, additional attribution, one or more visual representations of the first attribution and the second, additional attribution.