WO2024103112A1

WO2024103112A1 - System and method for identifying items and services of interest in an online environment

Info

Publication number: WO2024103112A1
Application number: PCT/AU2023/051156
Authority: WO
Inventors: Bronwen Cope
Original assignee: Bronwen Cope
Priority date: 2022-11-16
Filing date: 2023-11-15
Publication date: 2024-05-23

Abstract

The present invention relates to generating a curated set of retail item recommendations for a user including receiving a request from the user including the provision of details relating to the user and one or more social media accounts associated with the user, scanning the one or more social media accounts to identify, using recognition techniques, products and/or services in which the user has previously expressed interest, generating a curated set of retail item recommendations that includes a display of products and/or services of potential interest according to the user's previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts. Each product or service displayed having information and a link to a website, and periodically scanning the one or more social media accounts to identify the presence of any new or updated interests.

Description

SYSTEM AND METHOD FOR IDENTIFYING ITEMS AND SERVICES OF INTEREST IN AN ONLINE ENVIRONMENT

FIELD OF THE INVENTION

[0001] The present invention relates to a computer implemented system and method that provides a user with a curated set of retail item recommendations wherein the products and/or service are identified as being of potential interest to the user, according to their online activity.

BACKGROUND OF THE INVENTION

[0002] Although the internet has vastly expanded the way consumers are able to search for, locate and purchase items and services of interest, the copious amounts of online information that is available and the promotion of non-relevant items for display to consumers irrespective of the search query submitted makes it difficult for consumers to efficiently and effectively navigate the internet. As a result, consumers experience frustration when attempting to locate and purchase items of interest. Accordingly, users become disengaged from the online retail experience due to the frustration they experience.

[0003] Traditional search engines and other content-providing platforms also require users to devote considerable time and effort to navigate and scroll through numerous search results pages and images before locating relevant products and/or services of interest. Even when consumers are prepared to spend the time and effort to navigate and scroll through numerous search results pages and images, the search may be unsuccessful in the event the consumer is not sufficiently skilled in searching and/or does not use appropriate search terms to identify items and services of interest.

[0004] The amount of time and effort that a consumer is required to spend conducting key word searches and scrolling through numerous images, websites and information also gives rise to a wastage of computing processing and networking requirements which is clearly undesirable, particularly when considering that millions of individuals around the world are conducting such online searches. [0005] The present invention seeks to mitigate the problems discussed herein, or at least seeks to provide an alternative solution to existing online retail platforms, systems and methods.

[0006] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any suggestion, that the prior art forms part of the common general knowledge.

SUMMARY OF THE INVENTION

[0007] In one aspect, the present invention provides a computer-implemented method for generating a curated set of retail item recommendations for a user, the method including, receiving, by one or more processors, a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user, scanning, by the one or more processors, the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest, generating, by the one or more processors, a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service, and periodically scanning, by the one or more processors, the one or more social media accounts associated with the user to identify, the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.

[0008] In an embodiment, the curated set of retail item recommendations is provided for display on a data communications device associated with the user, wherein an executable software applicable is used to view the displayed curated set of retail item recommendations on the device.

[0009] In an embodiment, the request from the user to generate a curated set of retail item recommendations includes one or more of, a request from the user to access the software application or an interface thereof, a search query posted by the user using the software application, and a question posted by the user using the software application (eg. “best menswear to make an impression at a new job?”). [0010] In an embodiment, scanning the one or more social media accounts to identify the user’s previously expressed interest in a product and/or service of interest includes detecting one or more of, a previous search conducted by the user in respect of the product and/or service or similar products and/or services, a previous query submitted by the user in respect of the product and/or service or similar products and/or services, a previous social media interaction of the user in respect of the product and/or service or similar products and/or services, a previous social media post viewed, authored or shared by the user in respect of the product and/or service or similar products and/or services, a previous comment by the user in relation to a social media post in respect of the product and/or service or similar products and/or services, a previous purchase of the product and/or service or similar products and/or services by the user, and one or more preferences of the user which indicate an interest in the product and/or service or similar products and/or services.

[0011] In an embodiment, the previous interaction includes at least one “like” or “dislike” posted by the user in relation to the product and/or service or similar products and/or services in the one or more social media accounts.

[0012] In an embodiment, the one or more recognition techniques used to detect that a previous social media search, query, interaction, post, comment, purchase and/or preference of the user is in respect of the product and/or service or similar products and/or services includes artificial intelligence, machine learning and/or Natural Language Processing (NLP).

[0013] In an embodiment, the method further includes, determining, based on processing the detected previous social media searches, queries, interactions, posts, comments, purchases and/or preferences associated with the user, one or more of, context, relevance, a behavioural pattern of the user, and a sentiment of the user.

[0014] In an embodiment, generating the curated set of retail item recommendations for the user including a display of products and/or services identified as products and/or services of potential interest to the user is further based on one or more of the determined context, relevance, behavioural pattern and sentiment.

[0015] In an embodiment, the preferences of the user which indicate an interest in the product and/or service or similar products and/or services include one or more of, products and/or services of interest, or categories of products and/or services of interest, as reflected in a profile established by the user using the software application, products and/or services that the user prefers to exclude from the curated set of retail item recommendations as reflected in the user profile, and additional information reflected in the user profile and/or one or more images uploaded by the user, including information pertaining to the physical characteristics and/or preferred style of the user, including one or more of hair colour, skin tone, eye colour, height, weight, preferred clothing type, preferred clothing brand(s), clothing size, and/or information pertaining to forthcoming activities (eg. a forthcoming attendance at a wedding as a guest as identified from the user’s social media activity).

[0016] In an embodiment, the one or more social media accounts associated with the user are linked to the user profile, and the method further includes prompting the user to share particular products and/or services of interest and other product/service information encountered during their experience using the software application with one or more external connections.

[0017] In an embodiment, the retail item recommendations relate to fashion such that the user experiences a curated fashion experience. However, the platform may also provide retail item recommendations in other categories.

[0018] In an embodiment, the method further includes creating, by one or more processors, an outfit for the user based upon the user’s previously expressed interest in a product and/or service of interest including the preferences of the user relating to physical characteristics and/or preferred style of the user including one or more of hair colour, skin tone, eye colour, height, weight, preferred clothing type, preferred clothing brand(s), and clothing size, and/or the information pertaining to forthcoming activities; and providing, by one or more processors, the created outfit for display on the data communications device associated with the user.

[0019] In a particular embodiment, the outfit is created based on one or more of the user’s interests, the user’s identified likes, particular brand(s) identified as being of interest to the user and any forthcoming activities or events (for example, invitation to a wedding as a guest). [0020] In an embodiment, the display of the created outfit includes a link associated with each item that is comprised in the outfit, each link providing the user with access to information indicating at least a location at which the item is offered for sale and an associated purchase price.

[0021] In an embodiment, the method further includes, providing, by one or more processors, a payment gateway that enables the user to purchase the outfit or individual items thereof from one or more different retailers based on the location at which the item(s) are offered for sale and the associated price.

[0022] In an embodiment, where an outfit comprises multiple individual items, the payment gateway provides the user with the ability to purchase all items in a single transaction irrespective of whether the items are made available for purchase by multiple different retailers. In this embodiment, the curated retail platform may provide functionality that enables a user to purchase the entire automatically created outfit (for example, including skirt, top, jacket, shoes and handbag) including items from a number of different retailers (eg, retail websites) with a single selection. Further, the user may select one or more individual items from the displayed outfit to exclude from the transaction.

[0023] Accordingly, the curated system is able to effect an order without requiring the user to enter information regarding sizing and colours each time they place an order. It will be appreciated that the system and method may significantly conserve computer processing requirements since the requirement for the user to enter details regarding sizing, preferred colouring etc., is avoided each time the user places an order.

[0024] It will also be appreciated that by automatically creating an outfit for a user based upon the user’s specified and/or detected information, not only avoids the user wasting time searching for individual items of interest, but further conserves computer processing and network requirements since it avoids requiring the user to individually search the internet for individual items of interest.

[0025] In an embodiment, the outfit or individual items thereof is displayed in combination with other suggested items, thereby providing the user with additional styling suggestions and options. Alternatively, in embodiments, the user may manually control one or more aspects regarding the creation of an individual outfit and may submit a specific or vague selection of colours, materials, items, brands, styles etc and cause an outfit to be created based on providing manual input regarding selections (eg particular brand for blue ripped jeans with another particular brand for a white long sleeve shirt). Similarly, the user may create and view outfits for which the user manually provides a specific or vague selection of colours, materials, items, brands, styles etc and subsequently invokes the automated functionality of the system to complete creation of one or more outfits according to the manual selections provided by the user (eg wide leg long pants and crop top submitted as manual selection items with the system creating a variety of outfits according to the manually entered items with the automatically created outfits displayed to the user upon completion of the creation of those outfits by the software application).

[0026] In an embodiment, the method further includes prompting the user to select features associated with the item(s) comprised in the outfit prior to purchasing same, including one or more of the colour, material and size of each item, when such additional selections are available for purchase from the retailer.

[0027] In an embodiment, the steps of generating the curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user, and providing a link to a website associated with at least one retailer of each product or service, are further based on a location of the user (eg. if the user frequently attends a particular clothing store which sells a specific item, for example, leather jackets). In this regard, the user may be prompted to specify their location, or their locations may be automatically tracked, which may also be used to further filter the products and/or service item recommendations displayed, and may be used when directing the user to retailers of products and/or services that are of interest to the user.

[0028] In an embodiment, the additional activity of the user includes one or more of, the user following a new social media influencer, the user amending their relationship status in the one or more social media accounts, the user commencing new employment, the user expressing interest in a new hobby or activity, and the user amending one or more of their preferences. [0029] In an embodiment, the additional activity of the user is detected using recognition techniques including one or more of artificial intelligence, machine learning and/or Natural Language Processing (NLP).

[0030] In an embodiment, the generated curated set of retail item recommendations is presented in a scrollable display.

[0031] In an embodiment, the curated set of retail item recommendations includes one or more filtering categories that enable the user to filter the products and/or services displayed. Example filtering categories may include, but are not limited to, specific names of items, specific labels/brands, specific fashion styles (for example, “1960’s outfits”, gothic, punk, bridal) etc.

[0032] In an embodiment, selecting the link associated with a product and/or service further enables, in the absence of a retailer for the selected product and/or service, the generation of a list of additional retailers that offer similar items of potential interest.

[0033] In an embodiment, the method further includes automatically generating alerts to the user when new products or services, or updates with respect to existing products or services, that match the products and/or services of interest to the user are identified. Such updates may include, but are not limited to, sales or promotions regarding favourite brands and/or products, new posts from favourite influencers, new products from favourite brands, favourite items in new colours, sizes and types, and favourite products that have been restocked).

[0034] In an embodiment, the information associated with each product or service displayed in the curated set of retail item recommendations further includes links to reviews, posts and articles regarding the product or service as captured by content creators.

[0035] In another aspect, the present invention provides a computer-implemented system for generating a curated set of retail item recommendations, the system including one or more processors operable to, receive a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user, scan the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest, generate a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service, and periodically scan the one or more social media accounts associated with the user to identify, the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.

[0036] According to yet another aspect, the present invention provides a computer- readable medium including computer instruction code that, when executed by one or more processors, causes the one or more processors to perform the steps of, receiving a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user, scanning the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest, generating a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service, and periodically scanning the one or more social media accounts associated with the user to identify, the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.

[0037] In a further aspect, the present invention provides a portable electronic device for generating a curated set of retail item recommendations, the device including, a touch screen configured to receive an input corresponding to a touch operation of a user on an area of the touch screen, and a processor connected to the touch screen, wherein the processor is configured to detect the input to the touch screen and perform operations including, detecting a first input, via the touch screen, of a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user, scanning the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest, generating a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service, providing, for display on the touch screen, a graphical user interface (GUI) including the curated set of retail item recommendations, and periodically scanning the one or more social media accounts associated with the user to identify, the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Embodiments of the invention will now be described in further detail with reference to the accompanying Figures in which:

[0039] Figure 1 provides an overview of a system according to an embodiment of the present invention showing, in particular, the interaction between various system components;

[0040] Figure 2 illustrates a diagram associated with an exemplary server component of the system illustrated in Figure 1 ;

[0041] Figure 3 illustrates an exemplary flow diagram of a process that enables a user to download and install a software application, and subsequently access, or register to use, the software application for interaction with the system illustrated in Figure 1 , including for the purpose of creating a user account and profile;

[0042] Figure 4 illustrates an exemplary flow diagram of a process that enables a user to use the software application to scan their social media accounts to identify products and/or services of interest to the user and to generate a curated set of retail item recommendations for display to the user;

[0043] Figure 5 illustrates a diagram of an exemplary payment interface for handling financial transactions using the software application;

[0044] Figure 6 illustrates examples of additional software application interfaces in the form of search interfaces; and

[0045] Figure 7 illustrates additional example interfaces which enable the automatic creation of an outfit on behalf of the user, as well as providing assistance with respect to the location of particular items that form part of the outfit and are available for purchase, with the ability to share same with external connections. DETAILED DESCRIPTION OF EMBODIMENT(S) OF THE INVENTION

[0046] For simplicity and illustrative purposes, the present disclosure is described by referring to embodiment(s) thereof. In the following description, numerous specific details are set forth to provide a better understanding of the present disclosure. It will be readily apparent, however, that the current disclosure may be practiced without limitation to the specific details. In other instances, some features have not been described in detail to avoid the present disclosure.

[0047] According to an embodiment, the present invention relates to a computer- implemented system and method for generating a curated set of retail item recommendations (80), as shown by way of example in interface (190) of Figure 4. The system and method provide a platform that hosts a computer-executable software application (40), wherein the application (40) is accessible by a plurality of registered users (30). In particular, the system utilises a central server (20) in communication with a data communications device (50) associated with the user (30).

[0048] The central server (20) maintains one or more processors and/or databases for performing functions, including receiving, from a computing device (50) of a user (30), a request to generate the curated items display (190), wherein the request includes the provision of details relating to the user (30) including in relation to one or more social media accounts (60) of the user (30). This enables scanning of the one or more social media accounts (60) in order to identify, using one or more recognition techniques, products and/or services in which the user has previously expressed interest. The central server (20) may subsequently generate the curated set of retail item recommendations (80) for the user (30) that includes a display of retail item recommendations (80) identified as products/services of potential interest according to the user’s previously expressed interest with respect to the same or similar products and/or services in their one or more social media accounts (60).

[0049] The system determines that the user (30) has previously expressed interest in a particular product and/or service by, for example, recognizing the user’s interaction with their social media accounts, including in relation to the user posting one or more ‘likes’ (70), among other interactions as described in greater detail herein. Each product or service displayed in the curated set of retail item recommendations (80) may have associated therewith information regarding the product or service including a link to a website associated with at least one retailer of the product or service.

[0050] The server (20) also provides that the one or more social media accounts (60) are periodically scanned to identify the presence of new or updated interests of the user (30) with respect to products and/or services, as well as taking into account any additional activity of the user (30) with respect to products and/or services (e.g. the user (30) may follow a new influencer, change their relationship status, and/or update their employment details or details relating to their interests such as hobbies etc). In this way, the display (190) of the products and/or services in the curated set of retail item recommendations (80) may be updated according to any new/updated interests of the user (30) in addition to any newly recorded activities of the user (30).

[0051] The skilled person will appreciate that the platform addresses problems associated with traditional search engines and other content-providing platforms that require users to spend considerable time, effort and computing resources to navigate and scroll through numerous search result pages and images before locating products and/or services of particular interest. Such platforms also fail to automatically focus results such that the products and/or services listed take into account recent interests and activities of the user (30).

[0052] In an embodiment, the platform may relate to fashion such that the user (30) of the platform may experience a curated fashion experience. However, the platform may also specialize in other categories.

[0053] Figure 1 is divided into segments which are further expanded in subsequent Figures 2-7. In particular, Segment 200 of Figure 1 shows the server component (20) with which the software application (40) operating on data communication devices (50) of users (30) are configured to communicate. It will be apparent to the person skilled in the relevant field of technology that the software application (40) may be a mobile application or a web application, and similarly, the device (50) utilised by each user (30) may be a portable device such as a mobile phone, tablet or laptop, or alternatively a fixed location device such as a personal computer. The server component (20) is additionally detailed in Figure 2. [0054] The skilled person will appreciate that the steps described herein may be executed by the device (50) wherein such operations are facilitated by the software application (40) operating on each device. According to another implementation of the present invention, the server (20) is programmed to provide most or all of the functions described herein particularly where they cannot be provided locally on the user devices (50) or where it may be impractical to do so. In other words, the steps described herein as performed by the device (50), or components thereof, may be associated with hardware that is located externally of the device, such as the remote central server (20) for example (i.e. in a distributed architecture). Different arrangements are possible in this regard, and alternate variations will be apparent to the person skilled in the relevant field of technology.

[0055] Segment 300 of Figure 1 shows a user (30) downloading and installing the application (40) via interface (160) and subsequently accessing the application (40) via interface (170) in order to establish an account, as further detailed in Figure 3. Segment 300 also illustrates how user (30) may establish a user profile including by prompting the user (30) to provide details regarding products/services (or product/service categories) of general interest (55), details relating to one or more of the user’s social media accounts, information pertaining to the user’s physical characteristics/appearance, and to provide other general preferences to enable the user (30) to utilise the software application (40) in accordance with same.

[0056] Segment 400 of Figure 1 illustrates how user (30) may initiate via interface (180), scanning of their one or more social media accounts (60) utilising the software application (40) in order to generate the curated retail item recommendation interface (190), with the ability to adjust results by entering key words via filtering interface (210) and receive alerts through an alerts interface (220), as further detailed in Figure 4.

[0057] Segment 500 of Figure 1 illustrates an exemplary payment interface (230) enabling payments to be made using the software application (40), which is further detailed in Figure 5. Segment 600 of Figure 1 illustrates interfaces (240) which enable specific search queries to be entered by the user (30), and which further enable the user (30) to exclude particular items from their search results and/or their curated retail item listing, as further detailed in Figure 6 and described below. Finally, Segment 700 of Figure 1 illustrates the ability of the software application (40) to automatically create an outfit for the user (30) based upon the received account and profile information, as well as the scanned social media data, including the ability to locate items that form part of the outfit for purchase, or similar items as described below, and to share same with other social media connections, as detailed in Figure 7.

[0058] As mentioned above, Figure 2 shows in greater detail Segment 200 of Figure 1 and, in particular, Figure 2 shows the server component (20) which includes infrastructure upon which the platform of the present invention operates. The infrastructure (10) may be local or cloud-based.

[0059] The central server (20) may operate one or more computer processors and maintain one or more databases to enable the following functionality and/or storage:

• User account and profile register (100) storing details uploaded by each user (30) such as name, age, address, contact details, identifiers such as driver’s licenses or passport details, and any additional data which may be relevant for the purpose of identifying each user (30). Where possible, such details may be verified using identification verification services, and each user (30) may also be required to provide an image of themselves for further identification. The previously described profile information submitted by users (30), including physical characteristics and other preferences may also be stored in the user account and profile register (100);

• Social media account register (1 10) storing details relating to the one or more social media accounts (60) associated with each user’s account. This register (1 10) may also store additional social media-related information and data including the results of scanning the one or more social media accounts (60), previous search queries performed by each user (30) using the social media accounts (60), and details relating to the user’s interactions on social media including ‘likes’ (70) and other interactions which may be used to recognise the user’s interests and/or recent activities which may affect the platform’s identification I interpretation of products and/or services of interest to the user (30);

• Data processing functionality (1 15) for processing, using one or more recognition techniques (e.g. one or more recognition algorithms), the social media accounts (60) associated with users (30) in order to generate, based upon the products/services identified as being of potential interest to the user (30), a curated display (190) of retail item recommendations (80) for the user (30). The functionality (1 15) is also responsible for monitoring for updates and updating the display accordingly, e.g. updates based upon new/updated interests of the user (30) and any additional activity of the user (30) through their one or more social media accounts (60);

• Location database (120) storing historical and current location (90) details (eg. GPS coordinates) relating to users (30), which may be utilised to further enhance and/or filter curated search results as well as provide additional details to the user (30) such as map directions and the like, as described in further detail below;

• Payment gateway (130) for managing all financial transactions performed using the software application (40), including in relation to the purchase of displayed retail items (80), payment of subscription fees, etc.

[0060] Figure 2 also depicts that server (20) is configured to enable communication (140) with the user devices (50) and, in particular, the software application (40) operating on each user device (50). Such communications may occur via the internet or similar network.

[0061] Figure 3 shows in greater detail Segment 300 of Figure 1 and, in particular, the steps associated with a user (30) installing the application (40), which may be achieved by downloading the application (40) from an application store. Each user (30) may create their account using the application (40.) and the account information may be stored in the account register (100). As described above, the user account register (100) may capture information sufficient to enable each user (30) to be correctly identified.

[0062]The process of installing the application (40) is indicated by arrow (150), and interface (160) is also shown which allows each user (30) to download and install the application (40) in order to access the functionality thereof, including access to interface (170) used to create and maintain their account and profile. Once the application (40) has been accessed by user (30), the user (30) may be presented with interface (170), to allow the user (30) to create and maintain a user profile, including providing the user (30) with the ability to add/edit details and access additional functionality of the application (40) including their preferences with respect to products/services of interest (55), and details relating to their one or more social media accounts (60) (such as Facebook, Instagram, Pinterest, Twitter, etc).

[0063] In addition to specific products and/or services of interest (55), the user (30) may also specify broader categories of products and/or services of interest (e.g. clothing, holidays, etc) when establishing their profile, which may be used to further filter the products and/or services subsequently displayed (190) in the curated set of retail item recommendations (80). It will also become apparent that the user (30) may specify products and/or services, or categories of products and/or services, to exclude from the curated display (190).

[0064] During the account establishment process, the user (30) may also provide their address representing a primary location (90), however their location (90) may also be automatically tracked. The user’s “current” location may also be used to further filter the products and/or services subsequently displayed, and may be used when directing the user (30) to retailers of products and/or services.

[0065] Upon uploading sufficient information, each user (30) will be successfully registered such that the user (30) becomes a registered user who may then utilise the functionality of the application (40), which may be in accordance with a subscription level of the user (30).

[0066] Figure 4 shows in greater detail Segment 400 of Figure 1 and, in particular, the use of the application (40) by user (30) to scan their one or more social media accounts (60) using the social media scanning interface (180) in order to detect the user’s previously expressed interest in items (products and/or services). Such action may occur after receiving a request from the user (30) to generate a curated set of retail item recommendations for the user (30). Alternatively, no request is required to be manually entered by the user (30) since as soon as the user (30) accesses the software application (40), the curated set of retail item recommendations will be automatically generated and displayed. Other variations are possible, for example, the curated set of recommendations may be displayed based on the receipt of a search query from the user (30) or a question posted by the user (30), as described in greater detail below.

[0067] There are various ways in which previously expressed interest in a product or service by the user (30) may be ascertained. For example, this determination may be based on a previous search conducted by the user (30) for a product through their social media account(s) (60), a query submitted by the user (30) through their social media account(s) (60), a social media interaction of the user (30) such as a ‘like’ or “dislike”, a previous social media post viewed, authored or shared by the user (30), a previous comment by the user in relation to a social media post, a previous purchase by the user (30), and/or one or more preferences of the user which indicate an interest in a particular product and/or service.

[0068] The determination of previously expressed interest in a product and/or service by the user (30) enables data to be retrieved and processed using data processing functionality (115), which subsequently enables (eg. by the use of recognition algorithms) the generation of interface (190) representing a curated retail item recommendation interface. At a basic level, the curated interface will list retail item recommendations (80) identified as being of potential interest to the user (30) based upon the scanned social media account data, and interests in products/services detected in such data. A more advance filtering may take place when, for example, additional data is available. For example, context and relevance of user’s social media interactions may also be taken into account, and based on all received data, a behavioural pattern and sentiment of the user may be ascertained. The generation of the curated set of retail item recommendations (80) may be further based on one or more of the determined context, relevance, behavioural pattern and sentiment.

[0069] The user (30) may select to further filter the results including based upon their location (permanent, current or future) (90), and based upon other filtering criteria. For example, where the user (30) has an interest in clothing retail items, physical characteristic/appearance details may be entered by the user (30), wherein such characteristics may include hair colour, skin tone, eye colour, height, weight and/or clothing/shoe size may provide clothing retail items that will more closely match the user’s physical characteristics/appearance. [0070] Figure 4 shows an advanced filtering interface (210) enabling users (30) to add additional filters, including the ability to enter key word questions (e.g. ‘best menswear to make an impression at a new job?’) wherein, based upon the particular question posted, the retail items (80) displayed in the curated set of retail item recommendations (190) may be further filtered and updated according to the additional questions (e.g. the specific terms used in same).

[0071] The information associated with each product and/or service displayed in the curated set of retail item recommendations (80) may include price and/or retail information, as well as links to reviews, posts and articles regarding the particular product or service captured by content creators.

[0072] An alert interface (220) is also shown in Figure 4 which enables automatically generated alerts to be displayed to the user (30). Alerts may be generated based upon any number of factors, including, but not limited to, when new products or services, or updates with respect to existing products and services that match the products and/or services of interest to the user (30) are identified as being present in the curated listing of retail item recommendations (80). In this regard, the user (30) may specify ‘favourite’ retail items in their preferences in order to enable such alerts to be displayed whenever such ‘favourite’ retail items or categories appear in a newly curated item list. Such updates may include, but are not limited to, sales or promotions regarding favourite brands and/or products, new posts from favourite influencers, new products from favourite brands, favourite items in new colours, sizes and types, and favourite products that have been restocked.

[0073] As described earlier, the one or more social media accounts (60) associated with the user (30) may be periodically scanned to identify the presence of new or updated interests of the user (30) with respect to products and/or services, as well as any additional activity of the user (30) that may be relevant such that the display and/or services in the curated set of retail item recommendations (80) are updated according to any new/updated interests of the user (30) in addition to any recorded activity of the user.

[0074] In the examples referred to earlier, the system may identify that in a social media account associated with the user (30), the user (30) has elected to follow a new influencer, amended their relationship status, commenced new employment, expressed interest in a new hobby or activity, or amended one or more of their preferences. When such additional user activity has been identified, the curated list of retail item recommendations (80) may be automatically updated accordingly. For example, products and/or services associated with the new influencer followed by the user (30), or retail items considered to be relevant to the particular new employment and/or hobby commenced by the user (30), may be displayed or may appear earlier in the displayed list of items as compared with previously (i.e. certain items may receive a higher ranking).

[0075] Figure 5 shows in greater detail Segment 500 of Figure 1 , and, in particular, a payment interface (230) through which payments may be initiated in various circumstances using the software application (40), including, but not limited to, when the user (30) prefers to make a purchase of a particular retail item (80) through the software application (40), where a payment may be required to subscribe to the software application (40) or to enhance the subscription level of the user (30), management of any commission fees to an administrator of the software application, etc. The payment interface (230) and in particular the payment gateway (130) may also be configured to enable financial transactions using crypto currency.

[0076] Figure 6 shows in greater detail Segment 600 of Figure 1 , and, in particular, additional example interfaces (240) of the software application (40) in the form of search interfaces (240) which provide users (30) with the ability to search for particular products and/or services of interest and/or categories of products and/or services of interest. Any searching of this type undertaken through the software application (40) may also be monitored such that the data may be utilised to further filter the curated retail item listings in interface (190).

[0077] In addition to being able to exclude particular items or categories of items by specifying items to exclude in user preferences (eg. at the stage of establishing a user profile), the user (30) may also engage in the exclusion of particular items (or categories of items) when utilising the search interfaces (240) shown in Figure 6. In this regard, any search results will not include excluded items or items grouped into excluded categories of items. Figure 6 shows examples of an excluded category of item (i.e. ‘hats’) (85). It will be appreciated that by excluding such a category of items when conducting a search using interfaces (240) may also enable the curated feed (190) of retail item recommendations (80) displayed to the user (30), when updated based upon the search conducted through interfaces (240), to also exclude such items or categories of items.

[0078] Irrespective of whether the retail item recommendations (80) are presented to the user by way of accessing the curated items display (190) or a search undertaken by the user (30) using interfaces (240) or by any other means, it will be appreciated that discovering goods and services of potential interest to the user may be assisted by incorporating the capabilities of Artificial Intelligence (Al) models such as ChatGPT- 4 and Google DeepMind’s algorithms.

[0079] For example, Al models may be used to examine the user’s preferences, social media data and other data in a number of ways, identifying behavioural patterns and preferences. In one example, Google DeepMind algorithms may be used to analyse visual content from a user’s social media account for improved results and recommendations, whilst ChatGPT-4’s language understanding may decipher textual data in the social media account. In this way, personalised shopping experiences that adapt to the user’s shifting preferences and interests and lifestyle changes may be created, including through the use of deep neural networks which may learn from user interactions. Further, by combining ChatGPT-4's language understanding with DeepMind’s ability to process visual and/or audio data, a more comprehensive understanding of the user's preferences may be obtained, resulting in a shopping experience that is carefully curated to take into account a variety of sensory modalities.

[0080] In addition to using Al techniques to assist with the generation of results in response to a user request for a curated retail item listing, or in response to a search query, such techniques may also assist with responses to queries submitted through interface (210) shown in Figure 4. For example, ChatGPT-4’s advanced natural language processing (NLP) capabilities may enable user’s comments, posts and also natural language questions to be understood and processed, offering nuanced results that reflect the user’s objectives. The use of such techniques is also likely to discern the sentiment, context, and relevancy of such comments, posts and queries, to extract human interest signals beyond explicit “likes” or searches. [0081] The system may be able to recognise goods and services in pictures and videos that are shared on social media by the user (30) including, for example, based on Google DeepMind's proficiency with respect to computer vision. This would enable suggestions to be generated based on visual cues, in addition to textual interest signals.

[0082] Using past data and present trends, the use of Al may also forecast future interests. For example, the Al could predict new fashion trends and recommend products before they become popular by examining information from various sources. Predictive models may also be used to estimate pricing fluctuations, stock levels, and sales in order to proactively notify users (30) of opportunities.

[0083] When the social media accounts of the user (30) and additional activity of the user (30) are scanned to identify the presence of any new or updated interests of the user (30) with respect to products and/or services, the dynamic updating of the display (190) of retail item recommendations (80) in substantially real-time may be facilitated with the use of artificial intelligence capable of continuously learning from user interactions. For example, the items (80) and any associated suggestions would change in line with how the user (30) engages with and posts on social media. It will be appreciated that the more accurate, effective and relevant the results in interface (190), the less the user (30) will be required to carry out searching for items including through the searching interfaces (240), which reduces inefficiencies and wastage in computer processing resources. Further, since the system is able to comprehend a wider variety of information and queries and provide results relevant to the user's interests, this will reduce cognitive strain on users.

[0084] Al models are also able to generate comprehensive user profiles that take into account both expressed and implied preferences, resulting in a more accurate classification of user types and more focused item curation. These profiles may be used to tailor the user experience to specific tastes, even taking into account changes such as new interests or changes in lifestyle. Filtering options may also be improved by analysing user behaviour. Even in circumstances where users (30) do not explicitly express their preferences, their online activity can reveal interactions such as likes and dislikes, which can be recorded and added to the filtering algorithms. [0085] Since deep learning techniques in particular are very scalable in the field of artificial intelligence, it will be appreciated that as the user base of the platform expands, the system will become more efficient since increasing amounts of data may be managed without requiring a corresponding increase in processing time or resources.

[0086] Whether a user (30) chooses to buy a displayed item (80), reject a suggestion, or provide feedback, the system will be able to learn from their interactions with the recommendations. It will therefore be appreciated that by incorporating Al technologies such as ChatGPT-4 and Google DeepMind, users (30) of the platform will be offered a more individualised, effective, and contextually aware service.

[0087] Figure 7 shows in greater detail Segment 700 of Figure 1 and, in particular, an outfit creation interface (250). In this regard, the curated set of retail item recommendations (80) may be utilised to create an outfit (87) for the user (30) based upon styles that are identified as being of interest to the user (30). In one example, the user (30) may be prompted to create an outfit (87) based upon information extracted from scanning the user’s social media accounts (60) (e.g. likes/dislikes, their body shape, any identified interest/forthcoming activities (eg. a forthcoming wedding), etc. Additional factors which may be utilised when automatically generating an outfit for the user (30) include brands identified as being of interest to the user, previously entered information regarding their physical characteristics/appearance (eg. a photo of the user (30) which has undergone preprocessing (resizing, normalisation, etc), and processing through a computer vision service to identify products or fashion aspects), specified and/or collected/tracked information associated with the user (30), etc.

[0088] The integration of the abovementioned Al techniques may also assist the creation of ensembles for the user based, for example, on the user's visual media interactions, by identifying patterns, styles, and colours that match their preferences.

[0089] An outfit (87) created for a user (30) may comprise a single item such as the t- shirt depicted in interface (250) in Figure 4 or multiple items. The outfit (87) may include links associated with each item that forms part of the automatically created outfit (87), wherein the links include information that indicate at least a location (90) at which the particular item is offered for sale (either physical or online) and an associated price. Interface (260) shown in Figure 7 is an example of an interactive map that may be displayed to the user (30) showing the location (90) of outfits (87) or individual items thereof. Further, interface (270) may provide navigation assistance to the user (30) by providing directions from the user’s current location to a selected item location (90). Functionality may be provided that enables a user (30) to purchase the entire automatically created outfit (87) including items from a number of different retailers (e.g. retail websites) with a single selection.

[0090] Where stored physical characteristics/appearance of the user (30) is utilised to create the outfit (87), including for example any stored preferences regarding colours or styles, an order may be effected in respect of such items without requiring the user (30) to enter information regarding sizing and colours each time they place an order. Accordingly, computer processing resources will be conserved since the requirement for the user (30) to enter details regarding sizing, preferred colours etc is avoided each time the user places an order that avoids the user entering details repetitively.

[0091] Similarly, when collected/tracked information associated with the user (30) is utilised for such purposes, the user (30) no longer needs to waste time searching for individual outfit items of interest, hence computer processing and network resources are conserved since there is no longer a requirement for the user (30) to individually search the internet for individual outfit items of interest.

[0092] If the user (30) prefers to exclude one or more items which form part of the automatically generated outfit (87) from their order, change the colour of one or more specific items, and/or specify a different size, such actions may all be performed through interface (250) of the software application (40). The interface (250) may also display a specified, or identified, item of interest in combination with other suggested items, thereby providing the user (30) with styling suggestions and options.

[0093] It is to be understood that data pertaining to any interaction with search interfaces (240) and the automatic generation of outfits (87) for users (30) may also be passed back to the server (20) to further inform the curated listing of retail item recommendations (80) presented to the user (30).

[0094] Additional techniques may be utilised to enhance the experience of a user of the outfit creation interface (250) including through the use of Augmented Reality (AR) which may enable users (30) to virtually try on clothes, with the Al recommending pieces based on their body type, preferred appearance, and event requirements.

[0095]The interface (190) displaying the curated retail item recommendations (80) may be in the form of a scrollable display or other user-friendly display type. When a user (30) identifies a particular item of interest in the display, they may be prompted to select same and once selected, the user may be automatically presented with similar items available for purchase as well as information relating to other retailers that offer the same or similar items including information and/or links. The curated system may collect and store data with respect to items (products and/or services) that have been saved to the user’s ‘favourites’ list and/or purchased by the user (30) for the purpose of utilising that data to further filter the curated list (190) of retail item recommendations (80), as well as using the data for analytical purposes and/or advertising additional goods and/or services to the user (30).

[0096] A number of software development initiatives may be required to enable incorporation of relevant Al technologies via an Application Programming Interface (API). For example, the platform may use/in corporate one or more of: a) API Personalisation Endpoints for services that take user data as input and generate personalised product recommendations and predictive insights - the endpoints capable of processing both unstructured (eg. raw text from social media posts) and structured (eg. JSON, XML data including explicit customer preferences, historical purchase history, user demographics) data; b) NLP Integration of Services which enable building of natural language processing interfaces that comprehend and handle user enquiries, social media posts, and other textual data; c) topic identification, keyword extraction, and sentiment analysis capabilities; d) computer vision integration for creating API calls to computer vision services capable of recognising objects and fashion details in images and videos; e) means for handling resizing and normalisation of photographs as well as the safe transfer of multimedia files; f) means for creating and improving predictive models with the ability to assess user behaviour and offer suggestions based on historical data; g) Dynamic Learning System for building a feedback mechanism such that, in response to user engagement, the Al continuously learns and modifies its models; h) real-time updating system that allows recommendations to be changed or updated without suffering from major performance or outage issues; i) semantic search capabilities that combine semantic search services that comprehend the context and purpose of a user's search; j) means for improving the search algorithms' ability to comprehend natural language queries quickly and accurately; k) services for User Profiling and Segmentation to enable creation and management of comprehensive user profiles; l) means of developing APIs with the ability to accept data updates and modify user profiles as necessary; m) Improved Filtering Algorithms for creating algorithms that utilise behavioural analysis to apply sophisticated filters to product recommendations; n) means of verifying that the end user (30) can modify the filtering choices via the APIs; o) means of ensuring that Al integration is scalable (ie. can grow horizontally to support an increasing number of users); p) means of managing the scale, including but not limited to use caching, load balancing, and a microservices architecture; q) augmented reality service APIs that can interface with the front end to enable virtual try on of clothes, which also takes into consideration the processing and data transfer requirements for AR experiences that occur in real time; r) means of enabling the API to handle and examine inputs from multiple modalities such as text, graphics and audio; s) systems to record user comments via the API; t) means of ensuring data privacy and security, creating strong authentication and authorisations methods for API access; u) means of verifying that the handling and processing of data via APIs complies with applicable data protection laws (eg. CCPA, GDPR, etc); v) means of managing high request volumes, optimising APIs for low latency and high throughput; w) provision of API documentation including not limited to manuals, reference materials, and interactive testing tools for developers; x) means of tracking API performance, problems, and usage for continuous maintenance and troubleshooting.

[0097] Reference to structured and unstructured text in item (a) is expanded upon further below in the context of an embodiment involving offering users (30) with customised style suggestions and recommendations. According to this particular embodiment, recommendations may be provided to the user (30) based on the use of organised data points in the form of JSON or XML data that includes customer preferences including user identification, preferences, colours, sizes, brands, price range, purchase history, demographics and location. The following represents an exemplary query:

"userjd": "12345", "preferences": { "styles": ["casual", "bohemian"], "colours": ["earth tones", "pastels"], "sizes": ["M", "L"], "brands": ["BrandA", "BrandB"], "price_range": {"min": 50, "max": 200}}, "purchase_history": [ {"item d": "987", "date": "2023-01 -05"}, {"itemjd": "654", "date": "2023-02-15"} {"demographics": "age": 30, "location": "Berlin"

[0098] Accordingly, the query from the client includes user information and the current context. After being parsed (and preferably authenticated), the structured input may be entered into a recommendation engine (prediction model). Such a model may create a set of suggestions, including based on past performance and user behaviour. An exemplary output is reflected in the following JSON array which contains suggested products:

Copy code [ { "item d": "1 122", "name": "Bohemian Rhapsody Maxi Dress", "style": "bohemian", "price": 120 }, } "itemjd": "1334", "name": "Casual Cotton Tee", "style": "casual", "price": 60 } }]

[0099] It will be appreciated that the dress and tee which have been shortlisted above are inside the user’s price range and satisfy additional remaining preferences specified in the query. The above represents a simple example of a user query and response including recommendations. However, it will be appreciated that more complex queries and results may arise including those which also involve collaborative filtering processes (eg. a matrix factorization technique) and also based on user characteristics taken from purchase history and demographic data, and machine learning models used to generate predictions.

[0100] The following represents an example of a query based on unstructured data endpoints which are more complicated since the endpoints must decipher raw text data’s sentiment and context (this example based on unprocessed text from user- submitted material or social media post):

Copy this code: "userjd": "12345", "raw_content": "Enjoyed the flea market's retro ambience! The style of the 1970s is a whole attitude

[0101] Models for natural language processing (NLP) may be used to analyse the raw text. Entities and attitudes connected to style can be extracted using, for example, BERT or GPT-4, and understanding the user's positive connections with particular styles or eras (e.g., '70s fashion') may also be possible using sentiment analysis. Named entity recognition (NER) models may be utilised to locate particular style entities to associate with fashion goods in the database, such as "vintage vibes" or "flea market." The product database may then then be queried using the output from the NLP models to identify items that match the extracted entities and feelings. This may be achieved by employing either content-based filtering or similarity matching.

[0102] The below represents an exemplary JSON array containing product recommendations derived from the tone and context of the supplied text: Copy code [ { "item d": "221 1 ", "name": "Vintage Suede Jacket", "style": "70s fashion", "price": 150 }, { "itemjd": "331 1", "name": "Retro High-waist Jeans", "style": "vintage", { "price": 80 } }].

Accordingly, it will be appreciated that individualised insights and recommendations may be provided to users (30).

[0103] An API configured to deliver predictive insights may be trained, validated and built based on the data collected, including data relating to user interactions, preferences and behaviours. Such data may include but is not limited to interests in style, sizes, preference and demographics, behavioural data such as clicks, views, time spent on different products in social media sites, and past purchases, contextual data such as seasons, patterns, and preferences based on location, and feedback information including reviews, ratings and exchanges. The data may be subsequently processed to produce variables with meaning that predictive models can utilize (eg. “Average spend per category” or “frequency of purchases”).

[0104] The selection and training of machine learning models I algorithms used to forecast behaviour may be based on whether the task requires classification, regression or unsupervised learning. For example, a recommender system could be used utilising matrix factorisation or collaborative filtering approaches to forecast items that a user (30) may find appealing in light of comparable user preferences. In another example, classification models may be used to classify behaviour, employ ensemble techniques such as random forest and gradient boosting, decision trees, or logistic regression. As stated previously, deep learning may also be utilised to provide sequence predictions in user behaviour through the use of neural networks, eg. Recurrent Neural Networks (RNNs) or Long Short-Term Memory networks (LSTMs).

[0105] Once a model has been trained and verified, it may be placed into a production environment in which programmes can use APIs to access the model. In this regard, RESTful APIs may be queried by such programs to obtain predictions, and the API endpoint may provide a list of suggested goods or services as output after receiving user data and context as input. Such endpoints may be secured with appropriate authentication, eg. OAuth. Caching techniques for frequently requested predictions may also be used, and user sessions may be managed to deliver consistent experiences and enhance performance.

[0106] The model may be required to execute requests in batches or in substantially real-time depending on the requirements of the software application (40). When responding in real-time, the model responds with minimal latenc in order to deliver results (eg display of retail item recommendations (80)) instantly. To maximise resource utilisation, batch processing may be planned for off-peak hours for non- immediate insights such as daily style recommendations.

[0107] Continuous monitoring may be initiated after deployment to ensure the model operates as planned. For example, to keep track of the effectiveness of a model, errors, recalls, accuracy and precision may be tracked, and a feedback loop may be put in place for gathering user input so that the model can be further trained and improved. Further, pipelines may be used to feed new data into models on a regular basis or to adjust them according to changes in used preferences and/or trends.

[0108] Security and privacy concerns may also be addressed by ensuring that the model complies with ethical norms, privacy legislation (eg. CCPA or GDPR), etc.

[0109] It will be appreciated by persons skilled in the relevant field of technology that numerous variations and/or modifications may be made to the invention as detailed in the embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all aspects as illustrative and not restrictive.

[0110] Throughout this specification and claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising”, will be understood to imply the inclusion of a stated feature or step, or group of features or steps, but not the exclusion of any other feature or step or group of features or steps.

Claims

The claims defining the invention are as follows:

1 . A computer-implemented method for generating a curated set of retail item recommendations for a user, the method including: receiving, by one or more processors, a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user; scanning, by the one or more processors, the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest; generating, by the one or more processors, a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service; and periodically scanning, by the one or more processors, the one or more social media accounts associated with the user to identify: the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.

2. A method according to claim 1 , wherein the curated set of retail item recommendations is provided for display on a data communications device associated with the user, wherein an executable software applicable is used to view the displayed curated set of retail item recommendations on the device.

3. A method according to claim 2, wherein the request from the user to generate a curated set of retail item recommendations includes one or more of: a request from the user to access the software application or an interface thereof, a search query posted by the user using the software application, and a question posted by the user using the software application.

4. A method according to either claim 2 or claim 3, wherein scanning the one or more social media accounts to identify the user’s previously expressed interest in a product and/or service of interest includes detecting one or more of: a previous search conducted by the user in respect of the product and/or service or similar products and/or services, a previous query submitted by the user in respect of the product and/or service or similar products and/or services, a previous social media interaction of the user in respect of the product and/or service or similar products and/or services, a previous social media post viewed, authored or shared by the user in respect of the product and/or service or similar products and/or services, a previous comment by the user in relation to a social media post in respect of the product and/or service or similar products and/or services, a previous purchase of the product and/or service or similar products and/or services by the user, and one or more preferences of the user which indicate an interest in the product and/or service or similar products and/or services.

5. A method according to claim 4, wherein the previous interaction includes at least one “like” or “dislike” posted by the user in relation to the product and/or service or similar products and/or services in the one or more social media accounts.

6. A method according to either claim 4 or claim 5, wherein the one or more recognition techniques used to detect that a previous social media search, query, interaction, post, comment, purchase and/or preference of the user is in respect of the product and/or service or similar products and/or services includes artificial intelligence, machine learning and/or Natural Language Processing (NLP).

7. A method according to any one of claims 4 to 6, further including: determining, based on processing the detected previous social media searches, queries, interactions, posts, comments, purchases and/or preferences associated with the user, one or more of: context, relevance, a behavioural pattern of the user; and a sentiment of the user.

8. A method according to claim 7, wherein generating the curated set of retail item recommendations for the user including a display of products and/or services identified as products and/or services of potential interest to the user is further based on one or more of the determined context, relevance, behavioural pattern and sentiment.

9. A method according to any one of claims 4 to 8, wherein the preferences of the user which indicate an interest in the product and/or service or similar products and/or services include one or more of: products and/or services of interest, or categories of products and/or services of interest, as reflected in a profile established by the user using the software application, products and/or services that the user prefers to exclude from the curated set of retail item recommendations as reflected in the user profile, and additional information reflected in the user profile and/or one or more images uploaded by the user, including information pertaining to the physical characteristics and/or preferred style of the user, including one or more of hair colour, skin tone, eye colour, height, weight, preferred clothing type, preferred clothing brand(s), clothing size, and/or information pertaining to forthcoming activities.

10. A method according to claim 9, further including: creating, by one or more processors, an outfit for the user based upon the user’s previously expressed interest in a product and/or service of interest including the preferences of the user relating to physical characteristics and/or preferred style of the user including one or more of hair colour, skin tone, eye colour, height, weight, preferred clothing type, preferred clothing brand(s), and clothing size, and/or the information pertaining to forthcoming activities; and providing, by one or more processors, the created outfit for display on the data communications device associated with the user.

1 1. A method according to claim 10, wherein the display of the created outfit includes a link associated with each item that is comprised in the outfit, each link providing the user with access to information indicating at least a location at which the item is offered for sale and an associated purchase price.

12. A method according to claim 1 1 , further including: providing, by one or more processors, a payment gateway that enables the user to purchase the outfit or individual items thereof from one or more different retailers based on the location at which the item(s) are offered for sale and the associated price.

13. A method according to claim 12, where an outfit comprises multiple individual items, the payment gateway provides the user with the ability to purchase all items in a single transaction irrespective of whether the items are made available for purchase by multiple different retailers, and to select one or more individual items from the displayed outfit to exclude from the transaction.

14. A method according to either claim 12 or claim 13, further including prompting the user to select features associated with the item(s) comprised in the outfit, including one or more of the colour, material and size of each item, when such additional selections are available for purchase from the retailer.

15. A method according to any one of the preceding claims, wherein the steps of generating the curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user, and providing a link to a website associated with at least one retailer of each product or service, are further based on a location of the user.

16. A method according to any one of the preceding claims, wherein the additional activity of the user includes one or more of: the user following a new social media influencer, the user amending their relationship status in the one or more social media accounts, the user commencing new employment; the user expressing interest in a new hobby or activity, and the user amending one or more of their preferences.

17. A method according to claim 16, wherein the additional activity of the user is detected using recognition techniques including one or more of artificial intelligence, machine learning and/or Natural Language Processing (NLP).

18. A method according to any one of the preceding claims, wherein the generated curated set of retail item recommendations is presented in a scrollable display.

19. A method according to any one of the preceding claims, wherein the curated set of retail item recommendations includes one or more filtering categories that enable the user to filter the products and/or services displayed.

20. A method according to any one of the preceding claims, wherein selecting the link associated with a product and/or service further enables, in the absence of a retailer for the selected product and/or service, the generation of a list of additional retailers that offer similar items of potential interest.

21 . A computer-implemented system for generating a curated set of retail item recommendations, the system including one or more processors operable to: receive a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user; scan the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest; generate a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service; and periodically scan the one or more social media accounts associated with the user to identify: the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.

22. A computer-readable medium including computer instruction code that, when executed by one or more processors, causes the one or more processors to perform the steps of: receiving a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user; scanning the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest; generating a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service; and periodically scanning the one or more social media accounts associated with the user to identify: the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.

23. A portable electronic device for generating a curated set of retail item recommendations, the device including: a touch screen configured to receive an input corresponding to a touch operation of a user on an area of the touch screen, and a processor connected to the touch screen, wherein the processor is configured to detect the input to the touch screen and perform operations including: detecting a first input, via the touch screen, of a request from the user to generate a curated set of retail item recommendations, the request including the provision of details relating to the user including one or more social media accounts associated with the user; scanning the one or more social media accounts to identify, utilising one or more recognition techniques, products and/or services in which the user has previously expressed interest; generating a curated set of retail item recommendations for the user that includes a display of products and/or services identified as products and/or services of potential interest to the user according to the user’s previous expressed interest with respect to the same or similar products and/or services in their one or more social media accounts, each product or service displayed having associated therewith information regarding the product or service and/or a link to a website associated with at least one retailer of the product or service; providing, for display on the touch screen, a graphical user interface (GUI) including the curated set of retail item recommendations; and periodically scanning the one or more social media accounts associated with the user to identify: the presence of any new or updated interests of the user with respect to products and/or services, and additional activity of the user, such that the display of products and/or services in the curated set of retail item recommendations is updated according to any new/updated interests of the user in addition to any recorded activity of the user.