The present disclosure describes transaction-enabling systems and methods. A system can include a smart contract wrapper configured to access a distributed ledger including a plurality of embedded contract terms and a plurality of data values, interpret an access request value for the plurality of data values and, in response to the access request value, provide access to at least a portion of the plurality of data values, and commit an entity providing the access request value to at least one of the plurality of embedded contract terms.

An ad is placed in a movie, by analyzing inherent characteristics of the movie, analyzing viewed characteristics of the movie, analyzing viewer characteristics of a viewer of the movie, obtaining advertiser preferences for placement of the ad in the movie, determining costs of placing the ad in the movie based on the inherent characteristics of the movie, the viewed characteristics of the movie, the viewer characteristics and the advertiser preferences, and placing the ad in the movie in accordance with the inherent characteristics of the movie, the viewed characteristics of the movie, the viewer characteristics, the advertiser preferences and the determined costs.

Apparatus and methods present a content item and question from an inquirer to a group of users in a first feedback session with a requirement for the users to provide predictions of how a member of a distinct target group will respond to the content item, receiving, during the first feedback session, the first group's prediction of the target group's responses, presenting, during a second feedback session, the same content item and the same question to the target group with a requirement for the target group to provide responses directed to their own responses, constructing a predictive model of the target group based on responses received from the target group during the second feedback session, measuring accuracy of the first group's predictions using the target group predictive model and providing the inquirer access to an ordered visual representation of the first group users arranged as a function of accuracy.

The present teaching relates to obtaining a model for identifying content matching a query. Training data are received which include queries, advertisements, and hyperlinks. A plurality of subwords are identified from each of the queries and a plurality of vectors for the plurality of subwords of each of the queries are obtained. Via a neural network, a vector for each of the queries is derived based on a plurality of vectors for the plurality of subwords of the query. A query/ads model is obtained via optimization with respect to an objective function, based on vectors associated with the plurality of subwords of each of the queries and vectors for the queries obtained from the neural network.

Techniques for leveraging a social graph to facilitate the delivery of relevant recommendations. For example, a recommender is identified based on the recommender being a member of a social networking service who has interacted with an item of web-based content. A recommendee is identified based on the recommendee being another member of the social networking service who is connected to the recommender via a social graph maintained by the social networking service and based on having an affinity score for the item that exceeds a recommendee affinity score threshold and a connection strength to the recommender that exceeds a connection strength threshold. The recommender is sent a communication that invites the recommender to recommend the item to the recommendee. With some example embodiments, the communication is sent to the recommender within a pre-determined time measured from the time the recommender initiated an interaction with the item of web-based content.

Methods and apparatus to generate corrected online audience measurement data are disclosed. An example method includes determining a corrected audience count of streaming media for a demographic group by: calculating a deduplication factor for a demographic group using a first audience count, a second audience count, and a third audience count; estimating a fourth audience count for the demographic group, the fourth audience count indicative of a number of people who accessed the streaming media; estimating a fifth audience count for the demographic group, the fifth audience count indicative of a number of people who accessed text content; and applying the deduplication factor to a sum of the fourth audience count and the fifth audience count to determine the corrected audience count. The example method also includes generating ratings information for the streaming media based on subtracting the corrected audience count from the first audience count.

A system is disclosed for presenting advertisements for products and related products for a consumer based on the products being purchased.

A system may identify attributes of a geofence surrounding a location. The attributes of the geofence may represent merchant characteristics of a brick-and-mortar store and may further represent item characteristics of merchandise items available for sale from the brick-and-mortar store. A subset of users from a plurality of users eligible to receive the geofence may be selected based at least in part on matching the attributes of the geofence to preferences associated with each user among the subset of users. Geofence data representing the geofence may be sent to each of the client devices operated by the subset of the plurality of users. An indication may be received from a client device operated by a user of the user crossing into the geofence based on the geofence data. Lastly, a notification may be presented to the client device operated by the user.

Tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth. Tuning parameters designated for the look-alike model are ascertained and the look-alike model is built based on the target audience definition and the tuning parameters. The tuning parameters may include at least a setting selectable to control reach versus accuracy for the look-alike model. Segment data indicative of market segments generated according to the look-alike model may then be exposed for manipulation by a client. The manipulation may include selectable control over the tuning parameters to generate different look-alike groups from the segment data.

An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.