The disclosure is directed to various ways of improving the functioning of computer systems, information networks, data stores, search engine systems and methods, and other advantages. Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the platform or the system) that collectively enable, in one or more datastores (e.g., where each datastore may include one or more databases) and systems. A system and method for maintaining unified events such as maintaining an ontology data store and an event datastore. The event datastore may store event record instances of one or more different types of event records. The system and method may identify one or more types of event records that track a specific event type based on a specific event type of a new event instance.

Techniques for providing webpages based on tracking consumer device interactions are discussed herein.

A system for processing images captured in a retail store is provided. The system may include at least one processor configured to receive an image depicting a store shelf having at least one bottle displayed thereon and analyze the image to detect a representation in the image of the at least one bottle. The at least one bottle may have an outline design associated with a product shape. The at least one processor is also configured to identify in the image two outline elements being associated with the outline design of the at least one bottle. Each of the two outline elements may have a specific length. The at least one processor may further be configured to determine a size of the at least one bottle based on a comparison of the lengths of the two outline elements.

Embodiments included herein are directed towards a finite state machine based, temporal, path to purchase customer marketing method and system. The method may include generating a plurality of customer focused stimuli, wherein the stimuli include messaging content information, delivery mechanism information, delivery time and frequency information, and message presentation frequency. The method may further include transmitting the plurality of customer focused stimuli to a customer computing device and receiving customer response data in response to the plurality of customer focused stimuli. The method may also include performing response quantization operations on the customer response data within a finite state machine to generate a temporal sequence of personalized customer recommendations.

A computer-implemented method for efficiently estimating the number of unique elements in a collection of elements comprises generating, via hash logic, hash values associated with the elements. The hash values specify bit positions within an array of bits. Hash values output from the hash logic conform to a geometric distribution such that bit positions of the array of bits corresponding to lower orders bits are more likely to be generated than bit positions corresponding to higher-order bits. Bits of the array of bits corresponding to the bit positions are set. The number of bits of the array of bits that are set is counted. Estimation logic estimates the number of unique elements of the collection of elements as a function of the number of bits of the array of bits that are set.

Disclosed examples access first impression data representative of first impressions collected by an impression monitor system, the first impressions corresponding to the media accessed at a plurality of client devices; generate a panelist impressions composition by removing at least one duplicate impression from second impression data, the second impression data representative of second impressions logged by meters installed at the client devices, the duplicate impression corresponding to one or more accesses to the media represented in both the first impression data and the second impression data for a same audience member; determine an error value for the media based on the panelist impressions composition and a database proprietor impression composition, the database proprietor impression composition provided by a database proprietor for the media; and determine an error-corrected impression composition based on the error value and the panelist impressions composition.

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.

An example system disclosed herein includes programmable circuitry to identify donor adjustment factors and recipient adjustment factors used for correction of media impressions logged by a database proprietor, the donor adjustment factors including first donor adjustment factors associated with a first geographic region and second donor adjustment factors associated with a second geographic region, determine a first reduced donor factor set corresponding to ones of the first donor adjustment factors that satisfy a threshold, determine a second reduced donor factor set corresponding to ones of the second donor adjustment factors that satisfy the threshold, and generate imputation factors based on an aggregation of retained ones of the donor adjustment factors, the retained ones of the donor adjustment factors selected based on the first reduced donor factor set and the second reduced donor factor set, the imputation factors to reduce error in the correction.

Aspects of the present disclosure involve a system comprising a storage medium storing a program and method for predicting a conversion rate. The program and method provide for receiving, from an advertisement service, a bid to display a first advertisement at a computing device; determining, in response to receiving the bid, a set of features that relate to the first advertisement; providing the set of features to a machine learning model configured to output a predicted conversion rate for the first advertisement, the machine learning model having been trained based on multi-task learning using plural sets of features corresponding to plural second advertisements, the plural sets of features being associated with both click-through conversions and view-through conversions; and determining, based on the output of the machine learning model with respect to the set of features, the predicted conversion rate for the first advertisement.

An online system receives, from an entity, a content item to be presented to online system users, in which the content item includes a landing page to a third-party website. The system accesses the landing page, identifies a set of items included in it, and determines whether the landing page is configured for performing one or more types of conversions associated with each item. The system matches one or more of the items with one or more target objects based on the determination and associates the matched target object(s) with the content item. The system receives information describing one or more impression events associated with presenting the content item to a user and information describing a conversion associated with a target object associated with the content item performed by the user, applies an attribution model to determine a contribution of the impression event(s) to the conversion, and reports the contribution.