Abstract: A computer-implemented is disclosed. The method includes: receiving, via a client device, a product search query; identifying a first product attribute based on query terms associated with the product search query; obtaining at least one search result matching the product search query; obtaining a set of product images associated with the at least one search result; for each of the product images, determining a respective level of confidence based on an estimation as to whether the product image is associated with the first product attribute; ranking the product images based on the levels of confidence; selecting at least a subset of the product images based on the ranking; and providing, via the client device, an indication of the at least one search result and the associated subset of product images.

Abstract: There are many situations in which it is useful to identify similar entities or to quantify how similar one entity is to another. However, similarity is often subjective, making it challenging to determine the similarity of two entities computationally. In order to allow for determining the similarity of entities according to a customizable definition of similarity, a selection of features for determining similarity is received from a user device and a machine-learning model is trained to encode the values of the selected features for a particular entity as a representation. The representations of two different entities may be compared to determine the similarity of the two entities according to the selected features. The trained machine-learning model may, additionally or alternatively, be used for cluster analysis to identify similar groups of entities.

Abstract: A system and method are provided for identifying complementary objects from images and, in particular, to using images containing objects determined based on an input, to identify such complementary objects. The method includes determining one or more objects based on an input, using an input embedding associated with the input, and object embeddings associated with the one or more objects; determining a plurality of images containing the one or more objects; identifying complementary objects in the images; and providing an output collection of identified complementary objects.

Abstract: Computer-implemented methods and systems for colour-based image tagging and colour-based searching. The method may include identifying, using image analysis, one or more dominant colours of a product based on an image of the product and receiving selection of at least one of the one or more dominant colours. In response to receiving the selection of the at least one of the one or more dominant colours, a search for products matching the at least one of the one or more dominant colours may be initiated to obtain one or more results of the searching, the one or more results including at least one product matching the at least one of the one or more dominant colours.

Abstract: A data partitioning system receives an input dataset for e-commerce products, each sample containing attributes and associated values for each product including at least an image; represents each sample as a node on a graph to provide a graph of nodes for the dataset; measures a relative similarity distance between each pair of nodes based on comparing at least image values for the attributes; determines for each pair of nodes whether they are related if the similarity distance between them is below a defined threshold, and if related, generate an edge between them on the graph; group the connected nodes into a first or a second group such that the grouped nodes have no edges connecting them to nodes in the other group and have a shortest relative similarity distance with each other. The groups are used as training dataset and testing data sets for a supervised machine learning classifier.

Abstract: Methods and systems for automatically ordering a set of images for consistent user interface display. The methods may include receiving a set of further images related to a first record, the first record referencing an ordered set of existing images, each of the existing images being assigned one or more respective image attributes. It may include assigning, using image analysis, one or more respective image attributes to each image in the set of further images and comparing image attributes assigned to the further images with image attributes assigned to the existing images to determine, for each of the further images, a corresponding one of the existing images. The further images are then ordered based on the determined corresponding ones of the existing images and the ordering of those existing images in the ordered set of existing images, and displayed in order in a user interface.

Abstract: A computer-implemented is disclosed. The method includes: receiving, via a client device, a product search query; identifying a first product attribute based on query terms associated with the product search query; obtaining at least one search result matching the product search query; obtaining a set of product images associated with the at least one search result; for each of the product images, determining a respective level of confidence based on an estimation as to whether the product image is associated with the first product attribute; ranking the product images based on the levels of confidence; selecting at least a subset of the product images based on the ranking; and providing, via the client device, an indication of the at least one search result and the associated subset of product images.

Abstract: Computer-implemented methods and systems for colour-based image tagging and colour-based searching. The method may include identifying, using image analysis, one or more dominant colours of a product based on an image of the product and receiving selection of at least one of the one or more dominant colours. In response to receiving the selection of the at least one of the one or more dominant colours, a search for products matching the at least one of the one or more dominant colours may be initiated to obtain one or more results of the searching, the one or more results including at least one product matching the at least one of the one or more dominant colours.

Abstract: A computer-implemented is disclosed. The method includes: obtaining video data and audio data for a live media stream; detecting a first product in at least one video frame of the live media stream, the first product being one of a first set of defined objects associated with the live media stream; identify one or more keywords in speech detected in the audio data, the one or more keywords being included in a second set of defined terms associated with the live media stream; determining a product variant of the first product based on the detected one or more keywords; and providing, for display via a client device associated with a viewer of the live media stream, an interactive user interface element associated with a first action in connection with the product variant.

Abstract: A data partitioning system receives an input dataset for e-commerce products, each sample containing attributes and associated values for each product including at least an image; represents each sample as a node on a graph to provide a graph of nodes for the dataset; measures a relative similarity distance between each pair of nodes based on comparing at least image values for the attributes; determines for each pair of nodes whether they are related if the similarity distance between them is below a defined threshold, and if related, generate an edge between them on the graph; group the connected nodes into a first or a second group such that the grouped nodes have no edges connecting them to nodes in the other group and have a shortest relative similarity distance with each other. The groups are used as training dataset and testing data sets for a supervised machine learning classifier.