Patent Description:
The disclosure herein generally relates to the field of e-commerce and, more particularly, to a method and system for personalized outfit compatibility prediction.

Recommendation systems (RecSys) play a vital role in ecommerce platforms. A significant percentage of sales on these platforms is based on recommendations. RecSys improve user's shopping experience by helping them to find relevant products. User's feedback and purchase history help RecSys to recommend suitable products to the users. Recently, RecSys have been adapted for various business domains, e.g., video streaming services, fashion, grocery, etc. In fashion e-commerce platforms, recommendation systems also recommend fashion outfits apart from complementary and substitute products. A fashion outfit is a set of fashion items or apparel that are worn together and represent a particular style.

Unlike visual similarity, visual compatibility is a complex concept in outfit recommendation. Determining visual similarity between fashion items involves comparing visual attributes of the items, e.g., the color of the shirt and trousers. Visual similarity helps in fashion item search and substitute item recommendation. Visual compatibility uses latent concepts to determine the compatibility between fashion items. In the case of fashion outfits, all the items should be visually compatible with each other.

Existing approaches for outfit compatibility prediction can be grouped into two types: (i) methods without personalization and (ii) methods with personalization. The main aim of methods without personalization is to learn an optimal representation of the items or outfits. The methods in this group ignore individual users' preferences and implicitly capture population preferences. There are some approaches for personalization too. However, those conventional personalization based methods fail to capture relationship between fashion items. US patent application <CIT> is background art.

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a method for personalized outfit compatibility prediction is provided. The method includes receiving, by one or more hardware processors, a plurality of images pertaining to a plurality of outfits, wherein the outfit defines a set of fashion items representing a particular style. Further, the method includes extracting via the one or more hardware processors, a visual embedding corresponding to each of the plurality of outfit using a visual feature extractor, wherein each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits. Furthermore, the method includes generating via the one or more hardware processors, a fully connected graph for each of the plurality of outfits based on the visual embedding using a Graph Neural Network (GNN) with an attention mechanism. Furthermore, the method includes generating via the one or more hardware processors, an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN. Furthermore, the method includes simultaneously generating via the one or more hardware processors, a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network. Furthermore, the method includes computing via the one or more hardware processors, a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique. Finally, the method includes predicting via the one or more hardware processors, a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected.

In another aspect, a system for personalized outfit compatibility prediction is provided. The system includes at least one memory storing programmed instructions, one or more Input /Output (I/O) interfaces, and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to receive a plurality of images pertaining to a plurality of outfits, wherein the outfit defines a set of fashion items representing a particular style. Further, the one or more hardware processors are configured by the programmed instructions to extract a visual embedding corresponding to each of the plurality of outfit using a visual feature extractor, wherein each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits. Furthermore, the one or more hardware processors are configured by the programmed instructions to generate a fully connected graph for each of the plurality of outfits based on the visual embedding using a Graph Neural Network (GNN) with an attention mechanism. Furthermore, the one or more hardware processors are configured by the programmed instructions to generate an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN. Furthermore, the one or more hardware processors are configured by the programmed instructions to simultaneously generate a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network. Furthermore, the one or more hardware processors are configured by the programmed instructions to compute a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique. Finally, the one or more hardware processors are configured by the programmed instructions to predict a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected.

In yet another aspect, a computer program product including a non-transitory computer-readable medium having embodied therein a computer program for personalized outfit compatibility prediction is provided. The computer readable program, when executed on a computing device, causes the computing device to receive a plurality of images pertaining to a plurality of outfits, wherein the outfit defines a set of fashion items representing a particular style. Further, the computer readable program, when executed on a computing device, causes the computing device to extract a visual embedding corresponding to each of the plurality of outfit using a visual feature extractor, wherein each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to generate a fully connected graph for each of the plurality of outfits based on the visual embedding using a Graph Neural Network (GNN) with an attention mechanism. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to generate an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to simultaneously generate a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network. Furthermore, the computer readable program, when executed on a computing device, causes the computing device to compute a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique. Finally, the computer readable program, when executed on a computing device, causes the computing device to predict a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected.

Unlike visual similarity, visual compatibility is a complex concept. Determining visual similarity between fashion items involves comparing visual attributes of the items, e.g., the color of the shirt and trousers. Visual similarity helps in fashion item search and substitute item recommendation. Visual compatibility uses latent concepts to determine the compatibility between fashion items. In the case of fashion outfits, all the items should be visually compatible with each other.

Existing approaches for outfit compatibility prediction can be grouped into two types: (i) methods without personalization and (ii) methods with personalization. The main aim of methods without personalization is to learn an optimal representation of the items or outfits. The methods in this group ignore individual users' preferences and implicitly capture population preferences. There are some approaches for personalization too. However, those conventional personalization based methods fail to capture relationship between fashion items. To overcome the challenges of the conventional approaches, embodiments herein provide a relative score based method and system for personalized outfit compatibility prediction. The present disclosure proposes an approach to model the user's preference for different styles. The outfit compatibility prediction module is a critical component of an outfit recommendation system. An outfit is said to be compatible if all the items are visually compatible and match the user's preferences. Various factors such as demography, season, occasion, and user preferences, affect the outfit compatibility score. The outfit compatibility is subjective, i.e., an outfit liked by one user need not necessarily be preferred by another user. The use of population preferences for outfit recommendations leads to sub-optimal results. Therefore, it is necessary to consider the user's preferences for compatibility scoring. The present disclosure represents the outfit as a graph and uses graph neural network (GNN) with an attention mechanism (e.g., dot attention, multi-head attention, and self attention) to capture the interrelationship between the items. A graph read-out layer generates the final outfit embedding. The proposed approach efficiently models the preferences of the users for different styles. Finally, the outfit compatibility score is generated by computing the similarity between the outfit embedding and the style-specific user embedding.

<FIG> is a functional block diagram of a system <NUM> for personalized outfit compatibility prediction, in accordance with some embodiments of the present disclosure. The system <NUM> includes or is otherwise in communication with hardware processors <NUM>, at least one memory such as a memory <NUM>, an I/O interface <NUM>. The hardware processors <NUM>, memory <NUM>, and the Input /Output (I/O) interface <NUM> may be coupled by a system bus such as a system bus <NUM> or a similar mechanism. In an embodiment, the hardware processors <NUM> can be one or more hardware processors.

The I/O interface <NUM> may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface <NUM> may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I/O interface <NUM> may enable the system <NUM> to communicate with other devices, such as web servers, and external databases.

The I/O interface <NUM> can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I/O interface <NUM> may include one or more ports for connecting several computing systems with one another or to another server computer. The I/O interface <NUM> may include one or more ports for connecting several devices to one another or to another server.

The one or more hardware processors <NUM> may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors <NUM> is configured to fetch and execute computer-readable instructions stored in the memory <NUM>.

The memory <NUM> may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory <NUM> includes a plurality of modules <NUM>. The memory <NUM> also includes a data repository (or repository) <NUM> for storing data processed, received, and generated by the plurality of modules <NUM>.

The plurality of modules <NUM> include programs or coded instructions that supplement applications or functions performed by the system <NUM> for personalized outfit compatibility prediction. The plurality of modules <NUM>, amongst other things, can include routines, programs, objects, components, and data structures, which performs particular tasks or implement particular abstract data types. The plurality of modules <NUM> may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules <NUM> can be used by hardware, by computer-readable instructions executed by the one or more hardware processors <NUM>, or by a combination thereof. The plurality of modules <NUM> can include various sub-modules (not shown). The plurality of modules <NUM> may include computer-readable instructions that supplement applications or functions performed by the system <NUM> for personalized outfit compatibility prediction. In an embodiment, the modules <NUM> include a visual embedding extraction module <NUM> (shown in <FIG>), a Fully connected graph generation module <NUM> (shown in <FIG>), an outfit embedding generation module <NUM> (shown in <FIG>), a style-specific user embedding generation module <NUM> (shown in <FIG>), a visual compatibility score computation module <NUM> (shown in <FIG>) and a visually compatibility outfit prediction module <NUM> (shown in <FIG>). In an embodiment, <FIG> illustrates a functional architecture of the system of <FIG>, for personalized outfit compatibility prediction, in accordance with some embodiments of the present disclosure.

The data repository (or repository) <NUM> may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) <NUM>.

Although the data repository <NUM> is shown internal to the system <NUM>, it will be noted that, in alternate embodiments, the data repository <NUM> can also be implemented external to the system <NUM>, where the data repository <NUM> may be stored within a database (repository <NUM>) communicatively coupled to the system <NUM>. The data contained within such an external database may be periodically updated. For example, new data may be added into the database (not shown in <FIG>) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS).

<FIG> is an exemplary flow diagram illustrating a method <NUM> for personalized outfit compatibility prediction implemented by the system of <FIG> according to some embodiments of the present disclosure. In an embodiment, the system <NUM> includes one or more data storage devices or the memory <NUM> operatively coupled to the one or more hardware processor(s) <NUM> and is configured to store instructions for execution of steps of the method <NUM> by the one or more hardware processors <NUM>. The steps of the method <NUM> of the present disclosure will now be explained with reference to the components or blocks of the system <NUM> as depicted in <FIG> and the steps of flow diagram as depicted in <FIG>. The method <NUM> may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, etc., that perform particular functions or implement particular abstract data types. The method <NUM> may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network. The order in which the method <NUM> is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method <NUM>, or an alternative method. Furthermore, the method <NUM> can be implemented in any suitable hardware, software, firmware, or combination thereof.

At step <NUM> of the method <NUM>, the one or more hardware processors <NUM> are configured by the programmed instructions to receive a plurality of images pertaining to a plurality of outfits. The outfit defines a set of fashion items representing a particular style as shown in <FIG>. Now referring to <FIG>, outfit-<NUM> defines a set of fashion item and outfit-<NUM> defines another set of fashion items. For example, an outfit is mathematically represented as O = {I<NUM>, I<NUM>,. IN}, with N fashion items, Ii ∈ RHxWx3, where i = <NUM> to N. Here, H and W represents height and width of the image. Let Ul represents the lth user in the system.

At step <NUM> of the method <NUM>, the visual embedding extraction module <NUM> executed by one or more hardware processors <NUM> is configured by the programmed instructions to extract a visual embedding (xi) corresponding to each of the plurality of outfit using a visual feature extractor. The visual feature extractor can be a Convolution Neural Network (CNN) or a vision transformer. Each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits. The visual feature extractor is trained using a plurality of positive outfits and a plurality of negative outfits corresponding to each of the plurality of users until a minimum ranking loss is obtained. For example, ResNet-<NUM> without classification layer is used in present disclosure. The output of global average pooling is fed to a Fully Connected (FC) to obtain the visual embedding of the item. The visual embedding is mathematically represented as given in equation (<NUM>), where fv is the feature extractor, xi ∈ Rdemb, wherein demb is the dimension of visual embedding.

At step <NUM> of the method <NUM>, the fully connected graph generation module <NUM> executed by the one or more hardware processors <NUM> is configured by the programmed instructions to generate a fully connected graph (G = V, E) for each of the plurality of outfits based on the visual embedding using a Graph Neural Network (GNN) with an attention mechanism. Here, V represents nodes and E represents edges.

For example, the GNN with an attention mechanism can be one of a dot attention mechanism, a multi-head attention mechanism, and a self-attention mechanism. The GNN with an attention mechanism is trained using a plurality of positive outfits and a plurality of negative outfits corresponding to each of the plurality of users until a minimum ranking loss is obtained. For example, the ranking loss can be a Bayesian personalized ranking loss, a triplet ranking loss and a margin loss. The fully connected graph includes a plurality of nodes and a plurality of edges connecting the plurality of nodes, wherein each of the plurality of nodes represents a visual embedding of fashion items in the outfit as shown in <FIG>. Now referring to <FIG>, the outfit <NUM> is given as input to the visual feature extractor <NUM>. The visual embedding obtained from the visual feature extractor <NUM> is converted into the fully connected graph <NUM>.

Let, X = {x<NUM>; x<NUM>;. xN}, X ∈ Rdemb represent the embedding of nodes. In an embodiment, the dot attention mechanism is used to update the embeddings of the nodes. DOT-GNN uses a dot attention mechanism to capture visual interaction between the items. The updated node embedding is given by equation (<NUM>), where Xnew is the updated embedding of nodes and θgnn represents the trainable parameter of DOT-GAT, <MAT> represents the graph with updated node embedding. The updated equation for DOT-GNN is given in equation (<NUM>). Now referring to equation (<NUM>), Ni represents the set of neighbor nodes of i. GNN with attention mechanism provides different weightage (<NUM> ≤ αij ≤ <NUM>) to neighbor nodes of a node. This attention plays a vital role in capturing asymmetrical importance of items in the outfit. <MAT> <MAT> <MAT> <MAT> <MAT>.

At step <NUM> of the method <NUM>, the outfit embedding generation module <NUM> executed by the one or more hardware processors <NUM> is configured by the programmed instructions to generate an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN. The output of the graph read out layer is given in equations (<NUM>) and (<NUM>). Here, q ∈ Rdemb represents the outfit embedding and the fr represents the read-out function. <MAT> <MAT>.

In the present disclosure, each user is associated with an embedding ul ∈ Rdemb. To capture users' preferences for different styles or contexts, the user embedding is transformed using the feature transformation network ft. The feature transformation block transforms the user embedding using 'K' learnable style embedding Ck ∈ Rdemb, yielding 'K' style conditioned embedding Ulk ∈ Rdemb for each user, wherein ulk = ft(ul, ck; θft); for k = <NUM> to K, ft(ul, ck) = ωT(ul + ck) + b. Here, θft = {ω ∈ Rdembxdemb, b ∈ Rdemb} represents the trainable parameters. Both user (ul) and style embedding (ck) are randomly initialized. Then, with the aid of implicit feedback of the users on the outfit, these embedding are updated using Bayesian personalized ranking (BPR) loss. In an embodiment, the dimension of user embedding (ul) and style embedding (ck) are set equal to the dimension of outfit embedding (q), i.e., demb = <NUM>. The number of style embedding was set as K = <NUM>.

At step <NUM> of the method <NUM>, the style-specific user embedding generation module <NUM> executed by the one or more hardware processors <NUM> is configured by the programmed instructions to simultaneously generate a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network. For example, the feature transformation network can be a fully connected neural network. The feature transformation network is trained using a plurality of positive outfits and a plurality of negative outfits corresponding to each of the plurality of users until a minimum ranking loss is obtained.

At step <NUM> of the method <NUM>, the visual compatibility score computation module <NUM> executed by the one or more hardware processors <NUM> is configured by the programmed instructions to compute a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique. For example, the similarity based matching techniques includes cosine similarity and Euclidean distance. The compatibility score <MAT> of an outfit (o) to a user (ul) is computed by taking the cosine similarity (CS) between outfit embedding (q) and style-conditioned user embedding (ulk), given by equation (<NUM>).

Training: Let <MAT> represents the positive outfit set (outfit liked by user ul) and <MAT> represents the negative outfit set. Considering the mini-batch B with M samples, each sample represents a tuple (op, on, u) where <MAT> and <MAT> are positive and negative outfit for user ul. The proposed model is trained in an end-to-end manner. First, the compatibility scores for positive and negative outfits are generated. Then using Bayesian personalized ranking loss, the entire network along with the user and style embeddings are updated. The training loss is given by equation (<NUM>). Here, <MAT> represents compatibility score of the positive outfit and <MAT> represents compatibility score of the negative outfit.

<FIG> is an experimental result showing visual compatibility score for a plurality of users implemented by the system of <FIG> according to some embodiments of the present disclosure. Now referring to <FIG>, the visual compatibility score for users u504, u495, u310 and u507 for various outfits are illustrated. It has been observed that the visual compatibility score for the user pairs (u504, u495) and (u310, u507) are similar.

At step <NUM> of the method <NUM>, the visually compatibility outfit prediction module <NUM> executed by the one or more hardware processors <NUM> is configured by the programmed instructions to predict a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected.

Dataset: In an embodiment, Polyvore-U dataset is used for the experimentation and validation for the method (as disclosed in <NPL>). The details of the dataset are given in Table I. The dataset contains outfits collected by multiple users ('U'). Here, 'U' indicates the number of users. Polyvore-<NUM> and <NUM> contain outfits of fixed length. Polyvore-<NUM> and <NUM> contain outfits of variable lengths. Polyvore-<NUM> and <NUM> datasets are used for evaluation in the cold start setting.

Metrics: For evaluation, the present disclosure utilizes the ranking metrics like (i) Normalized Discounted Cumulative Gain (NDCG) and (ii) Area Under the ROC curve (AUC). The test set is ranked in descending order using the predicted compatibility score, and the ranking metrics AUC and NDCG are used for evaluation. Two settings were considered for evaluation, details given in Table II. During training, the ratio of positive to negative samples is <NUM>:<NUM>. For evaluation, this ratio is set to <NUM>:<NUM>. In protocol <NUM>, negative outfits are randomly created by sampling fashion items of different categories (i.e., top, bottom, and shoe). In protocol <NUM>, outfits of other users are sampled to create negative samples for a given user. ResNet-<NUM>, pre-trained on ImageNet is used for feature extraction. The user and style embedding are randomly initialized. The present disclosure is trained using an Adam optimizer with a learning rate of 1e-<NUM>. The images are normalized with mean and standard deviation same as that used for pretraining of ResNet-<NUM>. For data augmentation, the present disclosure perform random horizontal flip during training. The training mini-batch size is set to <NUM>.

The present disclosure handles new users without retraining the entire network. It is common for an e-commerce platform to encounter new users. The scenario is referred to as a new user cold start problem where the RecSys has to cater to new users with little or no interaction data (purchase, viewed, add to cart). The outfit compatibility prediction module which is an integral part of the fashion outfit recommendation system should be able to handle new users.

The embodiments of present disclosure herein address the unresolved problem of personalized outfit compatibility prediction. The present disclosure represents the outfit as a graph and uses GNN with an attention mechanism to capture the relationship between fashion items. A graph read-out layer is used to generate the final outfit embedding. Style-specific user embeddings are generated using the proposed feature transformation network. The outfit compatibility score is generated by computing the similarity between the outfit embedding and the style-specific user embeddings.

It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein such computer-readable storage means contain program-code means for implementation of one or more steps of the method when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g. any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g. hardware means like e.g. an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. Alternatively, the embodiments may be implemented on different hardware devices, e.g. using a plurality of CPUs, GPUs and edge computing devices.

The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. The term "computer-readable medium" should be understood to include tangible items and exclude carrier waves and transient signals, i.e. non-transitory.

Claim 1:
A processor implemented method (<NUM>), the method comprising:
receiving (<NUM>), via one or more hardware processors, a plurality of images pertaining to a plurality of outfits, wherein the outfit defines a set of fashion items representing a particular style;
extracting (<NUM>), via the one or more hardware processors, a visual embedding corresponding to each of the plurality of outfit using a visual feature extractor, wherein each of the visual embedding pertains to each of the plurality of images associated with each of the plurality of outfits;
generating (<NUM>), via the one or more hardware processors, a fully connected graph for each of the plurality of outfits based on the visual embedding using a Graph Neural Network (GNN) with an attention mechanism;
generating (<NUM>), via the one or more hardware processors, an outfit embedding based on fully connected graph associated with each of the plurality of outfits using a graph read out layer associated with the GNN;
simultaneously generating (<NUM>), via the one or more hardware processors, a style-specific user embedding corresponding to each of a plurality of users based on user information and style information corresponding to each of the plurality of users using a feature transformation network;
computing (<NUM>), via the one or more hardware processors, a visual compatibility score between each of a plurality of outfit embedding and each of the plurality of style-specific user embedding using a similarity based matching technique; and
predicting (<NUM>), via the one or more hardware processors, a visually compatible outfit for each of the plurality of users based on the computed visual compatibility score, wherein an outfit with the visual compatibility score above a predefined threshold is selected.