Systems and methods are disclosed for online distribution of content based on a user sentiment index. The method may include receiving, over a network and from a user device, one or more user generated inputs and calculating the user sentiment index based on the one or more user generated inputs. The method may also include receiving, over the network, from a content or advertising provider, instructions on publishing content or advertising to a webpage based on the calculated user sentiment index, and publishing content for display on user devices over the network based on a comparison of the calculated user sentiment index and the received instructions.

A social networking system associates identification information with combinations of values used to generate a dynamically-created advertisement. Interactions with the dynamically-created advertisement by social networking system users are also associated with the dynamically-created advertisement. The social networking system uses the identification information to present the same combination of values used to generate the dynamically-created advertisement to additional social networking system users. Additionally, information describing interactions with the dynamically-created advertisement is determined from the interactions associated with the identification information, allowing the dynamically-created advertisement to be included in a personalized feed of content items presented to an additional user along with information describing interactions by various users presented with the same combination of values of components in the dynamically-created advertisement.

The disclosure extends to methods, systems, and computer program products for identifying transaction data from within aggregated data and suggesting additional services based on the transaction data. The disclosure also extends to methods, systems, and computer based products for discovering transaction attributes within a database of aggregated account data including, aggregating and matching financial account attributes between a plurality of accounts in order to provide additional services to account holders.

A computer-implemented system including a data store: a content items database, a user account database, and one or more servers configured to execute a computer program to perform one or more of the following: receiving content items from content database that are associated with a topic selected by a user for posting on a social network, wherein at least one content item is associated with an URL; estimating a post to a reaction filter for a time interval for the social network for the user, calculating a reaction profile associated with reactions to posts on the social network by aggregating reaction time of a plurality of users on the social network for one or more content items posted on the social network; determining a schedule for posting the content items on the social network as a function of the post to reaction filter and reaction profile.

A product selecting unit extracts a candidate for recommended product on the basis of browsing information in a browsing information storing unit and unpurchased product information in an unpurchased product information storing unit. Product specifications that are information specific to each product are stored in a product information storing unit. A specification correlation calculating unit extracts a product having a product specification correlated with a product specification of the candidate for recommended product on the basis of the product specifications, and extracts the product as a recommended product.

A method for producing a well includes receiving production information associated with wells within a field; deriving a field specific model from the production information; receiving production information associated with the well; projecting production changes associated with installing artificial lift at the well at a projected date, the projecting using a production analysis engine applied to the field specific model, the projecting including determining a set of artificial lift parameters; and installing the artificial lift at the well in accordance with the artificial lift parameters.

A computer-based content delivery method includes receiving from a first user a request for content; based on said request, delivering particular content to said first user, wherein said particular content was selected based on information associated with a second user distinct from said first user. The first second users are members of a similarity group, and the second user is a leader of said group and said first user is a follower in said group.

Examples described herein relate to a system consistent with the disclosure. For instance, the system may comprise a data lake including information relating to an in-store activity of a customer and an online activity of the customer, a processing resource, and a non-transitory machine-readable medium storing instructions executable by the processing resource to identify the in-store activity and the online activity of the customer, aggregate and store the in-store activity and the online activity of the customer in the data lake, reduce an amount of products in a product portfolio, compare purchases including in-store purchases and online purchases, and recommend a product of the plurality of products based on the comparison.

The present invention relates to systems and methods for nutrient scoring according to labelled nutrients on a food or beverage product. The advantage of the present invention and its systems and methods for nutrient scoring is that it permits a simplified way of assessing the nutrient profile of a food or beverage product only based on the labelled nutrients.

Ads are generated based on product info and consumer profiles. A discriminator evaluates probabilities of ads being effective at causing consumer engagement. A decoder extracts product info from generated ads. Based on the probabilities of ads being effective and similarity of extracted and source product info, generated ads are labeled as examples. The examples are used in training an improved ad generator. Ads may be visual and/or audio containing speech. Ads may even contain humor, as recognized by mismatches between source and decoded product info.