Systems and methods herein describe a listing auto-generation system for generating a listing title and listing description for a listing in an online marketplace. The listing auto-generation system analyzes a set of listing data associated with the listing and generates the listing title and listing description using a machine learning model. The generated listing title and listing description are validated against a set of validation rules and presented on a user interface.

Methods and systems for publishing a playlist are disclosed. A user generates or selects a playlist, which is then provided (e.g., uploaded) for publishing. A playlist identifying at least one of one or more tracks and one or more albums is received. The playlist may then be published such that the playlist is viewable by one or more individuals. A user may then purchase one or more tracks/albums identified in the playlist via an online store.

Systems, device and techniques are disclosed for multi-label product categorization. A catalog entry and a list of categories may be received. The catalog entry may be associated with an item. A textual description may be generated by comparing words in the catalog entry to existing vocabularies of words and applying part-of-speech tagging to the catalog entry. A feature vector may be generated from the textual description by applying any of token frequency feature creation, term frequency-inverse document frequency feature creation, and pre-trained word embeddings to the textual description. A set of probabilities may be determined by inputting the feature vector into a machine learning model. The set of probabilities may include a probability for each category in the list of categories.

Methods, systems, and computer-readable media for catalog item selection based on visual similarity are disclosed. A similarity detection system selects candidate items for an unavailable item in a catalog. The candidate items are selected from one or more item categories in which the unavailable item is classified. The system determines respective similarity scores for at least some of the candidate items with respect to the unavailable item. For a particular candidate item, the similarity score is determined based at least in part on a visual similarity between an image of the unavailable item and an image of the particular candidate item. The system selects, from the candidate items, a set of visually similar items to the unavailable item based at least in part on the similarity scores. The visually similar items are available to the user.

An unrecognized item code is identified during a transaction at a transaction terminal. When the unrecognized code is determined to be associated with a typographical error, alternative corrected item codes are supplied to the transaction terminal to replace the unrecognized code. When the unrecognized code is determined to be missing from a product catalogue, an ordered list of most likely corrected item codes for the unrecognized code is provided to the transaction terminal for selection of one of the corrected item codes by an operator.

Methods and systems for balancing online stores amongst servers. Detecting a flash sale associated with a first online store. Identifying an occurrence of a first event correlated to the flash sale associated with the first online store. Identifying a second online store associated with a second event corresponding to the first event. Responsive to identifying the second online store associated with the second event corresponding to the first event, moving the second online store from a first server to a second server.

The present disclosure is a part identification and location system that has a server and a handheld device, and the handheld device is communicatively coupled to the server via a network. Further, the system has a handheld device processor that displays a plurality of graphical user interfaces to the handheld device and receives data from a user of the handheld device identifying a system, subsystem, or part that the user desires to purchase. Additionally, a server processor searches for the desired system, subsystem, or part on a plurality of part databases, locating one or more parts that match the system, subsystem, or part the user desires. The handheld device processor displays a list of the system, subsystems or parts located, receives data indicating which system, subsystem, or part the user desires to purchase, and receives payment data for purchasing the system, subsystem, or part.

A system and related method for completing online transactions via mobile device includes a client-side e-commerce platform executing on the mobile device that identifies candidate items for purchase based on images related to an item desired by the user. The client-side platform determines attributes of the item based on an image captured by an onboard camera or otherwise stored on, or downloaded to, the mobile device. Purchase information for items offered by participating vendors, or third-party vendors, is retrieved by the platform via APIs of the vendor network or automated middleware respectively. The user may select items for purchase and complete the transaction via single click, and the middleware forwards payment details to the third-party vendors and retrieves purchase information and transaction records from the third-party vendors. The client-side platform generates a single transaction and invoice whether the selected items are purchased from in-network or third-party vendors.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving from an item catalog database a respective item description and respective attribute values for each item of a set of items; generating text embeddings using a text embedding model to represent the respective item description and the respective attribute values; generating a graph of the set of items from the item catalog database connected by a set of edges; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, based on the machine learning model, as trained, a gender label for each first item in which the gender classification is unlabeled and in which a respective quantity of respective attribute values for the each first item is at least a predetermined threshold. Other embodiments are disclosed.

A method and apparatus for generating recommendation data for cataloging items in an e-procurement system is provided. In various embodiments, a database of records is created and maintained corresponding to a plurality of transactions in an e-procurement system. In various embodiments, database records are weighted and sorted according a transaction method associated with the records. In various embodiments, recommendation data is generated for items associated with the records to suggest more efficient methods for offering items for procurement in an e-marketplace based on the weights and sort order of the records.