A system and method to attribute expiration dates and quantities of a product to an SKU code for pricing purposes. This method applies dynamic pricing to perishable food items. Using bulk tagging capabilities, the supermarket can attribute expiration dates to items without the pain of actually tagging the items with stickers.

Systems, methods and products for cold start customization of a vehicle data system. In response to a consumer request to access a search tool, the consumer is identified and if there is no associated historical search information, a cold start customization process is performed. In this process, demographic information for the consumer is retrieved and used to identify specific groupings within several demographic categories which correspond to the consumer demographic information. The system then access a list which stores vehicle preferences for each possible demographic classification and retrieves the specific vehicle preferences (e.g., top-ranked vehicles for consumers in each classification) which correspond to the consumer's demographic classification. The system then generates a cold start search for the consumer which is customized according to the retrieved set of vehicle preferences corresponding to the consumer's identified demographic classification (e.g., includes the top-ranked vehicles for that classification).

Disclosed herein is a method for managing item recommendation using a degree of association between language units and usage history to manage recommendation of similar items with high probability of purchase, rather than a matching method expressed by keywords, recommendation management, by adding or deleting experience items using a vector model-based reasoning method based on a word-to-word association, in a scheme of planning a novel recognition system through the study of human emotions and tastes, T.P.O (Time, Place, Occasion) and various list-specific characteristics (color, texture, etc.) based on the language used in everyday life in consideration of language units and items preferred or experienced and/or purchased by a user, and of applying machine learning technology and natural language understanding technology.

Systems and methods to seed a search are described. The system identifies a seed listing included in listings that describe items being offered for sale on a network-based marketplace. The system identifies a seed filter context based on the seed listing and probabilities. The probabilities describe occurrences of attribute-value pairs in a plurality of listings that respectively describe items that were previously transacted on the network-based marketplace. The system extracts values from the seed listing based on the seed filter context. The system initializes the seed filter context based on the values and generates search results based on the seed filter context. The search results include a second plurality of listings that are identified from the first plurality of listings. Finally, the system communicates interface information to a client machine including the seed filter context and at least one listing from the second plurality of listings.

Systems and methods according to present principles involved highlighting objects such as on a store shelf. Highlighting is displayed by projecting a light directly on the object by means of a light source or may alternatively be indicated by displaying an indicator or image of the object on a display screen. Information about the targeted object may appear in a headmounted display, on a user's mobile device, or may be projected on a surface via the projector. Information provided to a user may include comparison product data, data about potential allergens, and the like. Highlighting can also be employed to suggest items of interest to the user. For example, products may be recommended to a user through analysis of user data and such products illuminated by a projector as a user walks through a store. Other highlighted objects may be those on a user grocery list or the like.

Disclosed herein are systems, methods, and computer-readable storage devices for a new browser including multiple application programming interfaces. A method includes receiving, from a site, at a browser and via a first application programming interface that defines a first protocol for communicating data between the browser and the site, a first payment request associated with a potential purchase by a user, in response to the first payment request and based on an identification of a payment service, communicating, from the browser and via a second application programming interface that defines a second protocol for communicating data between the browser and the payment service, a second payment request to the payment service, receiving, at the browser, from the payment service, via the second application programming interface, authorized payment information and communicating, from the browser, to the site and via the first application programming interface, the authorized payment information.

Disclosed is a method including presenting an input field on a user interface of a generalized search entity, wherein the generalized search entity processes data using a generalized search engine that indexes and searches both merchant sites and non-merchant sites, receiving user input in the input field and determining whether the user input corresponds to a product in a product database to yield a determination. When the determination indicates that the user input does correspond to the product in the product database, the method includes presenting a purchase-related search result, wherein the purchase-related search result is configured such that when a user interacts with the purchase-related search result and confirms a purchase associated with the purchase-related search result, the generalized search entity initiates a purchasing process for the product.

Various embodiments described herein utilize multiple levels of generative adversarial networks (GANs) to facilitate generation of digital images based on user-provided images. Some embodiments comprise a first generative adversarial network (GAN) and a second GAN coupled to the first GAN, where the first GAN includes an image generator and at least two discriminators, and the second GAN includes an image generator and at least one discriminator. According to some embodiments, the (first) image generator of the first GAN is trained by processing a user-provided image using the first GAN. For some embodiments, the user-provided image and the first generated image, generated by processing the user-provided image using the first GAN, are combined to produce a combined image. For some embodiments, the (second) image generator of the second GAN is trained by processing the combined image using the second GAN.

A method for returning a results page responsive to a user search query, such as a search query on a web site, may include receiving a search query from a user, determining, responsive to the query, a set of relevant products from a plurality of product listings based on a similarity of the user query to the respective product listings, retrieving inventory information respective of each of the relevant products, the inventory information comprising one or more available fulfillment channels respective of each of the relevant products, ranking the relevant products with respect to each other according to the inventory information, and returning, to the user, a search result comprising a list of the relevant products, ordered according to the ranking.

An online concierge system generates a graph connecting items with attributes of the items and other items. Hence, the graph includes nodes corresponding to attributes and nodes corresponding to items, with an item connected to attributes of the item in the graph. Example attributes include a brand, a category, a department, or any other suitable information about the item. When the online concierge system receives a search query to identify one or more items from a customer, the online concierge system parses the search query into combinations of terms and compares different combinations of terms to the graph to determine connections between different combinations of terms in the graph. Based on measures of connectedness between combinations of terms and connections in the graph, items are identified from one or more combinations of terms. Information about the identified items is presented to the customer.