Systems, methods, and computer-readable media for generating group-based sequential point-of-interest (POI) recommendations are disclosed. A primitive graph or sparse transition matrix is constructed that represents relationships between users, between POIs, and between users and POIs. A loss data prediction algorithm is executed on the primitive graph to obtain a filled graph, which may be a filled user-POI bipartite graph. A group sequential recommendation engine then performs an iterative sequential recommendation process that involves performing, at each iteration, various manipulations on the filled graph to obtain first a homogenous node type graph and then a minimum spanning tree. A next POI to recommend in a sequence of POIs is then determined from the minimum spanning tree. The iterative process continues until stopping criteria is met.

Implementations of the present specification disclose a method and a system for recommending a target transaction code setting region. The method includes the following: dividing a target region to obtain multiple sub-regions, where the multiple sub-regions include one or more label sub-regions with known target transaction code setting effects and one or more sample sub-regions with unknown target transaction code setting effects; obtaining an association feature between the multiple sub-regions; obtaining predicted effect values of setting a target transaction code in the one or more sample sub-regions by using a prediction algorithm based on at least estimated effect values of setting a target transaction code in the one or more label sub-regions and the association feature; and determining at least one recommended region for setting a target transaction code from the one or more sample sub-regions based on at least the one or more predicted effect values.

Embodiments provide computer apparatuses, computer systems and computer-executable methods for generating geographic zone information associated with a consumer. A method includes receiving a first geographic location associated with the consumer, and programmatically identifying a first geographic zone associated with the first geographic location. The method also includes programmatically generating a first importance score associated with the first geographic zone and associated with the consumer. The method also includes storing, on a non-transitory computer-readable medium, the first geographic zone and the first importance score associated with the consumer.

A method for facilitating online rental reservations from an online filter-deferred calendar for multi-instanced rental inventory includes the following steps. First, taking inventory of all item instances within a geographic region. Next, assigning a selection order to each item instance within the geographic region. Next, entering availability of each item instance on an individual availability calendar corresponding to each item instance. Next, displaying the individual availability calendar for the item instance with a highest selection order to a user via a user interface. Next, entering a start date and an end date for the item rental by the user via the user interface. Next, checking sequentially each item instance's individual availability calendar and determining a specific item instance that satisfies the entered start date and end date. The checking starts with the item instance having a lowest selection order. Next, reserving the specific item instance that satisfies the entered start date and end date. Finally, reassigning a new selection order to all item instances within the geographic region.

Method for monitoring the presence of a point-of-sale display in a shop, at the sight of consumers, the method comprising: acquiring signals from mobile devices by at least one signal sensor, sending one output from the signal sensor to an analyzing device, wherein the analyzing device: calculates an audience rate of the point-of-sale display on the basis of the output, and determines, on the basis of the audience rate, if the point-of-sale display is in the shop at the sight of consumers or not.

A device may detect a first entry of a first user device of a first user into a merchant area. The device may monitor a movement of the first user device within the merchant area. The movement may include a transition from a shopping area of the merchant area to a checkout area of the merchant area. The device may detect a transaction between the first user and the merchant. The device may determine a shopping duration for the first user and a checkout duration for the first user. The device may detect a second entry of a second user device of a second user into the merchant area. The device may perform one or more actions based on detecting the second entry. The one or more actions may be performed selectively based on the shopping duration or the checkout duration of the first user.

A multi-region risk relationship data store may contain electronic records representing a plurality of multi-region risk relationships and, for each multi-region risk relationship, an electronic record identifier and a set of multi-region attribute values including a plurality of hierarchical local risk relationship attribute values. A back-end application computer server may receive an indication of a selected risk relationship and display at least some of the associated multi-region attribute values. The server may receive adjustments to some of the multi-region or local risk-relationship attribute values, and displayed information may be automatically and dynamically configured based on the received adjustments and a standalone risk calculation unit. At least some local risk relationship attribute values may be replicated for multiple regions. When adjustments have been completed, the server may automatically assemble a multi-region risk relationship document.

Example embodiments of the disclosed technology implement a brand penetration determination system using image semantic content. A geographic sub-region determination system is configured to partition a geographic area into two or more sub-regions. An image content analysis engine is configured to determine, from images captured at one or more sites within each sub-region, a number of detections of a brand within each respective sub-region. A brand penetration index generation system is configured to generate a brand penetration index for each sub-region based on the number of detections of the brand in the respective sub-region weighted by one or more factors (e.g., population factor, category factor, etc.), which is stored in memory with an indicator of each respective sub-region. In splitting the geographic area into two or more sub-regions, the number and/or boundaries of sub-regions are determined so as to ensure that the population within each sub-region is above a threshold.

Embodiments of the present application provide a method, an apparatus, a device, and a storage medium for judging permanent area change, and relate to the field of computer technologies. By determining feature information corresponding to at least one candidate user, where any candidate user is a user whose permanent area changes with a probability greater than a first preset probability threshold, and furthermore, by inputting feature information corresponding to the above-mentioned at least one candidate user into a preset classification model, it can be judged whether a target permanent area of the above-mentioned at least one candidate user is changed.

A system and method utilizing a mass user gaze fixation behavior with respect to products and product racks (using a camera-based inputs and gaze-tracking algorithms) and a financial profitability of each product/product type; generates gaze-based heat maps for indicating blind spots and eye gaze points at different positions of the racks/shelves and indicating product placement positions on the heat map; and advises product placement in the retail store for optimizing financial profitability by performing an iterative refinement of product placements over a period for the product/similar products. The system translates mass eye gaze of retail store customers into financial profitability via product placement optimization, and iteratively runs a method to optimize product placements, and generate gaze-based heat maps and perform blind spot analysis for avoiding product placements at certain locations and placing products at other locations, e.g., by running a decision module on consumer gaze pattern data.