Walmart Apollo, LLC

Bodhisattwa Prasad Majumder

Abhinandan Krishnan

Shreyansh Prakash Gandhi

Rajesh Shreedhar Bhat

Amlan J Das

Ajinkya More

Anirban Chatterjee

Debanjana Banerjee

Gayatri Pal

Arunita Das

Aditya Subramanian

Subhasish Misra

Sumanth S Prabhu

Vignesh Selvaraj

Diptarka Saha

Examples provide a multi-stage cluster component that performs a multi-stage clustering analysis on a plurality of items in a category associated with a selected item using a set of interrelationship factors. The multi-stage cluster component generates a cluster of non-substitute item-pairs, a cluster of traditional substitute item-pairs, and a cluster of variety item-pairs. The set of interrelationship factors includes at least one of measure of association, brand similarity, pack-size similarity, demographic similarity, item description similarity, lift, and/or percentage same-basket variable. A propensity score is generated for each item-pair. The propensity score is utilized to identify traditional substitute items and variety substitute items. Each substitute item is ranked based on the generated propensity score. The ranking is used to identify potential low-performance items for removal from inventory.

System for calculating competitive interrelationships in item-pairs

The system and method described herein provide for a machine-learning model to automate determination of product attributes for a product based on images associated with the product. The product attributes can be used in online commerce to facilitate product selection by a customer. In accordance with this disclosure, the product attributes may be determined using machine-learning technology by processing images associated with the product (including product packaging). The machine-learning technology is trained using product-related vocabulary and potential attributes that can be discovered by analyzing the images associated with the product.

Automated extraction of product attributes from images

Examples provide a system for detecting anomalies in a dataset. The system includes one or more processors and a memory storing the dataset. The one or more processors are programmed to identify a first set of data points in a cluster, identify a second set of data points outside of the cluster as noisy data points, and determine whether each of the noisy data points is an anomaly by: determining a distance between the noisy data point and other data points in the dataset, ranking the distances between the noisy data point and the other data points, and applying a weight to each of the ranked distances to determine an outlier value for the noisy data point. When the outlier value for the noisy data point exceeds a threshold, the noisy data point is identified as an anomaly, and result is displayed in a user interface.

Relative density-based clustering and anomaly detection system

Some embodiments can comprise a system comprising one or more computer processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more computer processing modules a perform acts of: receiving, at the one or more computer processing modules and from a third-party electronic device, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules and using a sequence labeling model, a type of each token of the sequence of tokens; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate the type of each token of the sequence of tokens, wherein the type of each token of the sequence of tokens can comprise a BIO encoding scheme, wherein: a label B of the BIO encoding scheme can indicate a first token of a brand name; a label I of the BIO encoding scheme can indicate a subsequent token of the brand name; and a label O of the BIO encoding scheme can indicate a token that is not part of the brand name; determining, at the one or more computer processing modules, a brand name present in the title using each token of the sequence of tokens, as encoded; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the brand name present in the title; normalizing, at the one or more computer processing modules, the brand name present in the title to create a standardized representation of the brand name; writing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the standardized representation of the brand name present in the title to an empty database entry associated with the product; and in response to a search request from a user, transmitting instructions to a user display to display a representation of the standardized representation of the brand name for each token of the sequence of tokens. Other embodiments are also disclosed herein.

Method and system for attribute extraction from product titles using sequence labeling algorithms

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Kolkata, India

Bodhisattwa Prasad Majumder