The present invention relates to specifying commercial district information of merchants, and it pertains to the technical field of data processing. The method for specifying commercial district information of merchants according to the present invention includes the steps of: mining and calculating association rules of consumption and transaction data of consumers so as to obtain information of association between merchants; calculating rates of subordination of merchants whose commercial district information has not been specified to several commercial districts based on the association information and specified commercial district information of at least some of the merchants; and specifying the commercial district information for each of said merchants based on their rates of subordination. The present invention can realize automatic specification of commercial district information of merchants, and the specification of commercial district information is accurate and efficient.

An automated process to determine whether an image has been modified includes receiving an image (e.g., via a web portal), requesting an image validation service to analyze the image to determine whether the image and/or a subject depicted in the image, has been modified from its original form and, based on the analysis of the image validation service, outputting a likelihood that the image has been modified. The image validation service may analyze the image using one or more operations to determine a likelihood that the image has been modified, and provide an indication of the likelihood that the image has been modified to the web portal. The indication of the likelihood that the image has been modified may be presented on a display via the web portal, and various actions may be suggested or taken based on the likelihood that the image has been modified.

A system for training a bidding model comprising: a plurality of tactics stored on at least one database; a plurality of hyperparameters; in response to an available inventory from a publisher relayed through a real time bid server, computing a bid on the available inventory; sending the bid to the real time bid server; receiving an auction result in response to the bid; calculating a plurality of rewards based on the auction result and the tactics; calculate a plurality of q values based on the rewards; calculate a plurality of losses; backpropogating the losses through the bidding model.

Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.

The novel invention provides methods and systems for automated production of merchandising displays. The displays can be assembled in whole, shipped in whole and filled with customer-selected products in customer-selected amounts, filled with criteria-selected products in criteria-selected amounts or both.

A guided search system for suggesting and arranging filter criteria within a user interface for presentation to a user to help guide the user's search for listings is disclosed. The system builds one or more filter criteria frequency data structures indicative of the number of times each filter criterion has been used to filter search results and how often different filter criteria are used together. The system uses the frequency data structures to predict which filter criteria a user will likely employ to narrow their search given the filter criteria the user has already used. The system provides techniques for arranging or rearranging filter criteria within a user interface, by moving, placing, or ordering suggested filter criteria within the user interface, where a user is likely to be able to recognize and interact with the placed filter criteria, based on the determined amounts of use.

In an example embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.

Systems, apparatuses, methods, and computer program products are disclosed for generating a single-index model (SIM) tree. An example method includes receiving a data set and a maximum tree depth. The example method further includes screening a set of variables from the data set to form split variables. The method may include, while maximum tree depth has not been reached, (i) generating a fast SIM estimation for nodes of a tree level, (ii) for each node, selecting a split point and split variable based on the fast SIM estimation, (iii) based on the selected split points and split variables, generating nodes for a next tree level, each including a subset of data, and (iv) repeating steps (i), (ii), and (iii). The method may include fitting a SIM for each leaf node at maximum tree depth based on a subset of the data set represented by the leaf node.

Techniques are described for iteratively adjusting data processing decision results in accordance with rules. In some implementations, the applied rules may be data ethics rules associated with particular demographic groups, such as users in a particular geographic location, users in a particular age range, and so forth. The rules may describe the manner in which data, such as data that describes or identifies individuals, is collected, stored, analyzed, applied, manipulated, and/or destroyed. The various stages of data handling may be described as a data supply chain, and a set of rules may apply to the handling of data at one or more stages of the data supply chain. The rules may enforce data privacy considerations and/or other types of constraints on data handling.

Computer-based systems and methods for discovering neighborhood clusters in a geographic region, where the clusters have a mix of venues and are determined based on venue check-in data. The mix of venues for the clusters may be based on the social similarity between pairs of venues; or emblematic of certain neighborhood typologies; or emblematic of temporal check-in pattern types; or combinations thereof. The neighborhood clusters that are so discovered through venue-check in data could be used for many commercial and civic purposes.