A digital ad-buying platform uses counterfactual-based incrementality measurement by implementing randomization and/or a correction for auction win bias to avoid the need to identify counterfactual winner types in the control group. This approach can estimate impact at the individual consumer level. Confidence levels can be determined using Gibbs sampling in the context of causal analysis in the presence of non-compliance.

Methods and apparatus for determining a unique audience exposed to media while reducing memory resources of a computing device are disclosed herein. An example apparatus includes memory; instructions in the apparatus; and processor circuitry to execute the instruction to, based on impression requests from a plurality of client devices via a network, log a plurality of impressions corresponding to media accessed at the client devices; generate a product by multiplying a count of the impressions by a square of a count of registered users of a database proprietor exposed to the media; generate a quotient by dividing the product by a count of demographic impressions logged by the database proprietor; and determine a unique audience size based on a square root of the quotient.

The present disclosure provides for an on-line venue in which search results are displayed with rich media and qualified content and the ability to contact multiple of the sources anonymously or otherwise communicate what product or service they want to purchase by posting an interactive request that a number of sellers can then directly respond to. This enables a higher quality of web search results and for the buyer's to directly match their needs with qualified vendors. A buyer's request can be broadcast to relevant sellers or service providers that can be preselected by the buyer. Such embodiments may take the burden from a buyer in finding the right products and services by self-qualifying the request to proactive prospective sellers.

Keyword bids determined from sparse data are described. Initially, a portfolio optimization platform identifies which keywords included in a portfolio of keywords are low-impression keywords. This platform trains a machine learning model to generate bids for the low-impression keywords with historical data from a search engine. In particular, the platform trains this machine learning model according to an algorithm suited for training with sparse amounts of data, e.g., a temporal difference learning algorithm. In contrast, the platform uses different models, trained according to different algorithms than the low-impression keyword model, to generate bids for keywords determined not to be low-impression keywords. Once the low-impression keyword model is trained offline, the platform deploys the model for use online to generate actual bids for the low-impression keywords and submits them to the search engine. The platform continues to update the low-impression keyword model while deployed according to the sparse-data algorithm.

An ad collision machine can be configured to evaluate collision queries for possible ad collisions and is associated with an ad datacenter configured to evaluate and respond to bid requests on behalf of a plurality of advertisers. The ad collision machine can comprise a plurality of nodes and a data cache containing a plurality of user ID—campaign ID keys representing recently submitted bids in response to bid requests. Once a selected node receives a collision query, a user ID—campaign ID key is retrieved from the collision query. If the first key is not found in the data cache, it is written to the data cache by the node and the ad collision machine returns that user ID—campaign ID pair as available to be bid on.

The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Methods, systems, and apparatuses to match content providers and interested content users are described. Input indicating an accessing of a network location by a user is received along with the user's identifier. The identifier is obfuscated and transmitted to a content provider configured to provide content to the user at the network location. A re-direct identifier is transmitted to the user instructing the user to directly contact the content provider. When the user contacts the content provider, the user transmits a provider-specific identifier by which the content provider identifies the user and the obfuscated user identifier. The content provider updates a database of obfuscated user identifiers and provider-specific user identifiers based on the received identifiers. Thus, the content provider is enabled to identify interested users based on obfuscated and provider-specific user identifiers.

A navigation system can identify locations of interest at a route destination. Those locations of interest at the destination can fall within a predetermined distance of the destination or those that are responsive to a query that includes the destination as a parameter. The navigation system can receive a selection of an identified location of interest near the destination, and update a route to terminate at the identified location of interest rather than the original destination. Information identifying the origin and destination for the route can be collected at a first user device and the route can be displayed at a second user device. Additional information is then received from the first user device (such as a selection of an identified location of interest near the destination). This additional information received at the first user device is used to update the route that is displayed on the second user device.

A navigation system can identify locations of interest at a route destination. Those locations of interest at the destination can fall within a predetermined distance of the destination or those that are responsive to a query that includes the destination as a parameter. The navigation system can receive a selection of an identified location of interest near the destination, and update a route to terminate at the identified location of interest rather than the original destination. Information identifying the origin and destination for the route can be collected at a first user device and the route can be displayed at a second user device. Additional information is then received from the first user device (such as a selection of an identified location of interest near the destination). This additional information received at the first user device is used to update the route that is displayed on the second user device.

A navigation system can identify locations of interest at a route destination. Those locations of interest at the destination can fall within a predetermined distance of the destination or those that are responsive to a query that includes the destination as a parameter. The navigation system can receive a selection of an identified location of interest near the destination, and update a route to terminate at the identified location of interest rather than the original destination. Information identifying the origin and destination for the route can be collected at a first user device and the route can be displayed at a second user device. Additional information is then received from the first user device (such as a selection of an identified location of interest near the destination). This additional information received at the first user device is used to update the route that is displayed on the second user device.