Abstract: Described herein is a method and system for collection creation. The collection is a curated selection of product and/services presented to an online consumer. The products or services are derived from an electronic catalogue of an online store. The collection creation system comprises a processor, a collection creation application module installed on the online store, a database repository of templates, and a memory containing instructions, when executed by the processor, configure the system to apply the templates to the electronic catalog to automatically create curated collections of product items; and to activate the create collections and display the collections to the online consumer. A curated collection consists of a set of defined parameters for a particular theme. Curated collections enable the retailer’s consumers to browse the inventory of the retailer in an intelligent and thematic manner.

Abstract: Described herein is a system and computer implemented method for finding similars for a selected clothing image amongst a set of clothing images in an electronic catalog in an online store serving online customers. An object detection model is applied to extract the clothing section within the clothing images to create a preprocessed image. A first machine learning model is applied on the preprocessed image(s) to convert the colors and textures of said preprocessed image into a first set of vector representations. A second machine learning model is applied on the preprocessed image(s) to convert the shapes of said preprocessed image into a second set of vector representations. Operations of mapping nearest vectors, matching attributes, sorting and ranking are performed, and thereafter similar images are displayed to the online customer.

Abstract: Described herein is a system and computer implemented method of grouping clothing products by brands within a set of clothing images in an electronic catalog of an internet store serving online customers. Apply an object detection model to extract the dress section within the clothing image(s) to create preprocessed image(s). A machine learning model model is applied to the preprocessed image(s) to convert the image into a vector representation through an unsupervised technique. The vector contains the design features of the clothing image. The design features are representative of the brands. A clustering model is applied on the vector representations to arrive at the grouping of similar images of the clothing products. The grouped clothing products are displayed via a user interface, ordered by brands, to the online customers.

Abstract: Described herein is a method and system for collection creation. The collection is a curated selection of product and/or services from an electronic catalogue presented to an online consumer. A repository of templates is provided. The first set of attributes of the items of catalog is determined by an attribute classification model. In parallel, data relevant to items from media sources are extracted. Features are identified from the extracted data. A second set of preferred attributes from the identified features is identified. A common set of attributes amongst the first and second set of attributes is identified. Templates that match a minimum number of the template's attributes with the item's attributes are selected. The selected templates are applied to the items of electronic catalog to automatically create curated collections. Curated collections are activated and displayed to the online consumer on a user interface.

Abstract: Described herein is a method and system for creating templates. Themes are identified based on textual and image processing of media, wherein each of the themes is one or a combination of entities such as occasions, events, festivals, and seasonal wear. An attribute classification model is applied and thereafter attributes are identified based on textual and image processing of the media. Themes(s) are mapped to the attributes to create a template(s), wherein a map is a rule connecting themes to attributes. The template comprises a theme and its associated attributes, and the values of the attributes. A similarity search model is applied to create an extended range of templates from an initial template. The template is not a product and does not have a state. An extended range of templates are stored in a database.

Abstract: Disclosed herein is a computer implemented method of acquiring and utilizing individual style profiles for online gift shopping for a gift recipient and a gift buyer on a ecommerce website. Deep attribute and aesthetic level preferences of the gift recipient are captured via an interactive engagement module. The gift recipient is exposed to both visual and textual questions. The deep attribute and aesthetic level preferences of the gift recipient are applied to surface products for purchase by said gift buyer. When the gift buyer sets a budget, the preferences of the gift recipient's surface products for purchase by the gift buyer within said budget. When a group of gift recipients are considered, an integrated style profile is created and conflicts are resolved.

Abstract: Described herein is a method and system for collective shopping wherein one or more than one user is involved in the decision making process of an online purchase. The style profile is built for each individual contributor by exposing them to a set of product images and analysing their inputs about their preferences. The analysis is performed at an attribute level to understand each style aesthetic in a deep and meaningful manner. A backend algorithm runs on the attribute level feedback given by each contributor to calculate their combined style performance at an attribute and product level. This input is taken and run across the annotated online catalogue to surface products that fit the style aesthetic of the combined audience.

Abstract: Described herein is a method by which more than one person is enabled to actively participate in the process of finalizing a real estate property either for purchase or rent. Each deciding party is enabled to create a custom style profile capturing their individual preferences at an attribute level by providing both visual and verbal feedback. A Collective Preferences Profile (CPP) is created by integrating the multiple style profiles of the chosen deciding parties. The generated CPP is then utilized to curate the different houses available and to surface those houses that are most likely to fit the aesthetic and requirements of the combined audience. The CPP evolves on an ongoing basis by active solicitation of feedback on properties viewed or waitlisted to accommodate changing preferences and provide the most suited recommendation at any time.

Abstract: Described herein is a method for evaluating the performance gap of a proposed personalization solution versus a default solution without the need for integration. The method comprises utilizing the historical data, the data including a sample of engagement and transactions of a specific audience, and catalog feed; simulating the user actions in two environments, the environments being the proposed solution and the default solution; comparing the product exposure data between the two environments, and generating a report analyzing the number of sessions with transactions and/or engagement in each environment.