A social networking system dynamically adjusts a number of advertisements presented to a user along with organic content items by modifying a ranking including organic content items and advertisements. Partial engagement scores are generated for organic content items based on an expected amount of user interaction with each organic content item, and scores are generated for advertisements based on expected user interaction and bid amounts associated with each organic content item. An engagement score measuring the user's estimated interaction with a content feed including organic content items without advertisements and an additional engagement score measuring the user's estimated interaction with a content feed including organic content items and advertisements are determined from the partial engagement scores and the scores. A difference between the additional engagement score and the engagement score modifies a conversion factor used to combine expected user interaction and bid amounts to generate advertisement scores.

An online system generates a custom audience based on user engagement with distributed content items. The online system monitors and stores user engagement with content items. A content provider submits to the online system a request to generate a new custom audience and selects audience parameters and user engagement types, such as users who watched a specified amount or percentage of a video, users who interacted with an online system page associated with the content provider, or users who clicked through online system content to a page associated with the content provider. Responsive to the request, the online system retrieves the corresponding user engagement data and applies the audience parameters to generate the custom audience and present it to the content provider.

A method and apparatus for finding predictive cross-category search queries for behavioral targeting in a networked online display advertising system. The methods include aggregating a training model dataset, the training model dataset comprising a history of clicks corresponding to historical advertisements. The training model dataset also contains plurality of targeting categories related to the history of clicks. Various techniques are disclosed for selecting a plurality of features from the training model dataset and calculating a click probability for a subject advertisement to be clicked by a user from a page, the calculating operations using features of the page that is to be presented to the user. Embodiments include mapping a particular query to one of the targeting categories and then presenting the subject advertisement selected on the basis of the value of the click probability. Normalization scales down the value of the click probabilities to filter out false positive categories.

A computer-implemented method comprising: presenting, by a computing device, an advertisement on a first device; monitoring, by the computing device, viewing activity by a user of the advertisement, wherein the monitoring comprises tracking an amount of time that the user actively views the advertisement on the first device; and continuing, by the computing device, playback of the advertisement on one or more second devices until the user has actively viewed the advertisement for a target impression time based on the monitoring.

Television is the largest advertising category in the United States with over 65 billion spent by advertisers per year. A variety of different targeting algorithms are compared, ranging from the traditional age-gender targeting methods employed based on Nielsen ratings, to new approaches that attempt to target high probability buyers using Set Top Box data. The performance of these different algorithms on a real television campaign is shown, and the advantages and limitations of each method are discussed. In contrast to other theoretical work, all methods presented herein are compatible with targeting the existing 115 million Television households in the United States and are implementable on current television delivery systems.

This system is a set of newsstands connected to cloud-based servers. Each newsstand has at least one monitor. The servers deliver advertisements to the newsstands, which in turn schedule and display these advertisements. The newsstands are equipped with wireless signal sniffers, which record the number and duration of wireless signals generated by a smartphone or tablet within range of the stands. The newsstands send their advertising schedules and signal records to the cloud-based servers, and these servers process the data to determine the number of unique and repeat viewers of each advertisement.

Systems and methods for verifying an advertisement impression in a digital environment are provided. In some aspects, methods of the subject technology include operations for defining a portion of the digital environment as an impression area, wherein the impression area is associated with a tagged advertisement area, providing a stream of an advertisement to the tagged advertisement area, and updating advertising impression information stored in memory regarding the advertisement, wherein an advertising impression is based on the identification of the character within the impression area and the availability of an unobstructed line-of-sight between the character and the tagged advertisement area. In some aspects, computer readable media are also provided.

An automatic system facilitates selection of media properties on which to display an advertisement, responsive to a profile collected on a first media property, where a behavioral-targeting company calculates expected profit for an ad correlated with the profile and arranges for the visitor to be tagged with a tag readable by the selected media property. The profit can be calculated by deducting, from the revenues that are expected to be generated from an ad delivered based on the collected profile, at least the price of ad space at a media property where the BT company might like to deliver ads to the profiled visitor. When the calculated profit is positive (i.e., not a loss), the BT company arranges for the visitor to be tagged with a tag readable by the selected media property through which the BT company expects to profit.

Methods are provided for accomplishing computer-implemented methods of real-world conversion tracking. The methods include providing an electronic commerce advertisement for at least one product, receiving impression identification information and user identification information for the user, storing the impression identification information and the user identification information in a database, receiving an indication that the at least one product has been purchased by a purchaser at a physical store, identifying purchaser identification information associated with the purchase and received from the purchaser at the physical store, comparing the purchaser identification information for the purchaser with the user identification information for the user to determine whether the purchaser and the user identify a same person, and accordingly providing an indication of whether electronic commerce advertisement for the at least one product was viewed by the user prior to the user purchasing the at least one product in the physical store.

The subject personalized consumer advertising/ad placement system provides the ability for advertisers, ad agencies, and any other applicable organization to determine and electronically present their ideal consumer profile and have their advertisement/promotion placed in front of all consumers who match the profile based on the anonymous mining of the consumers actual spending across a broad base of spending categories.