An assortment planning method, an assortment planning system and a processing apparatus thereof for a smart store are provided. The assortment planning system includes at least one tracking apparatus, a plurality of detecting apparatuses, and a processing apparatus. The tracking apparatus is used to identify a plurality of consumer tracks. The detecting apparatuses are used to detect a plurality of consumer interactive behaviors of a plurality of products. The processing apparatus includes a binding device, an intention analyzing device and an estimating device. The binding device is used to bind the consumer interactive behaviors with the consumer tracks to obtain a number of interactive behavior time sequence records. The intention analyzing device is used to obtain a plurality of consumption intentions for the products according to the interactive behavior time sequence records. The estimating device is used to estimate a best product combination according to the consumption intentions.

A specific item is identified to suggest a replacement therefor to a user. A set of candidate replacement items for the specific item is determined. For at least one of the candidate replacement items, an expiration score is determined based on expiration information associated with the item. A replacement score for the candidate replacement item is determined by inputting the determined expiration score as a feature into a machine learning model that is trained using features of historical samples of candidate replacement items suggested as a replacement to users and the replacement suggestion being accepted by the users. One or more of the candidate replacement items is selected based on respective replacement scores as one or more suggested replacement items. A graphical user interface of a client device of the user is caused to display the one or more suggested replacement items as the replacement for the specific item.

Methods and systems, including computer-readable media, are described for generating recommended shopping trips. A computing system captures individualized shopper preferences associated with a user that include budget constraints and dietary restrictions. The method includes generating a personalized list of shopping items based on the individualized shopper preferences using a predictive recommendation engine and identifying, based on in-store attributes and inventory status, sequences of multiple shopping locations. Each sequence provides access to the shopping items. The method includes rendering the identified sequences on a user interface with contextual navigation aids.

A computing device obtains from a user a reference image of an individual depicting a desired cosmetic appearance through application of makeup products on the individual. The computing device obtains makeup products available to the user and identifies facial features of the individual. First looks among a collection of looks are identified based on attributes of the facial features of the individual. The computing device identifies reference makeup products based on the makeup effects applied in the first looks. The computing device identifies second looks among the first looks, the second looks having associated makeup products among the reference makeup products that closely match the makeup products available to the user. The computing device identifies compatible makeup products based on the makeup products associated with the second looks. The computing device obtains an image of the user and performs virtual application of the compatible makeup products on the user.

An object is to provide a store derivation device that derives an appropriate recommendation target to enable efficient recommendation. A recommendation system 100 that functions as a store derivation device of the present disclosure includes a visit history storage unit 106 configured to store visit history information of a store as an action history of a user. Further, a store acquisition unit 101 acquires a familiar store (degree of familiarity) of the user with respect to the store on the basis of the visit history information. The store acquisition unit 101 derives a visit candidate store fx1 that the user is recommended to visit, on the basis of the familiar store (degree of familiarity).

The disclosed technology provides for substituing items for an order fulfillment system. A method can include receiving, by a server system, order information from computing device for picking up an order at a fulfillment location indicating multiple items. The method further includes receiving, by the computing device, substitution option information from the server system for at least a subset of the multiple items of the order. The substitution option information indicates one or more substitution options for each of the subset of the multiple items. The method further includes receiving, by the server system, a second communication from the computing device indicating a selection of a substitution option for at least one item of the subset of the multiple items, and changing a display of the computing device to display a confirmation option selection based on receiving the substitution confirmation information.

Systems and methods for automatically recommending market locations and store assortments based on customer persona are disclosed. An example may involve: receiving a recommendation request; determining a persona basket including a list of relevant products based on the recommendation request; determining a list of customers having a customer persona associated with the persona basket; computing, for each location area, a persona concentration score indicating a concentration of customers of the customer persona in the location area; generating, based on the persona concentration scores, recommendation data regarding the customer persona; and transmitting the recommendation data to a computing device.

The system may create marketing campaigns based on inventory data and/or sales data for a product. The system may compare the inventory data for the product or the sales data for the product to inventory threshold data or sales threshold data, respectively. The system may adjust a marketing campaign for the product based on a relationship between at least one of the inventory data and the inventory threshold data, or the sales data and the sales threshold data. The marketing campaigns may be further based on weather data, traffic data, event data, seasonal data, holiday data, calendar data, trend data and/or any other data.

A system can include one or more memory devices storing instructions thereon that, when executed by one or more processors, can cause the one or more processors to receive a selection of a product, determine a status of the product, retrieve a plurality of descriptions of a plurality of products, the plurality of products having the first category, provide the plurality of descriptions and the description of the product to cause a machine learning model to generate one or more outputs that represent correlations between the product and one or more products of the plurality of products, identify the one or more products of the plurality of products, and provide a recommendation to replace the product with the one or more products of the plurality of products.