Description:RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, IC layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF INVENTION
[0002] The present disclosure relates generally to the field of E-commerce and identification of similar products listed on various E-commerce platforms. More particularly, the present disclosure relates to a system and method for identification of similar products in E-commerce and classifying products based on similarity/dissimilarity.

BACKGROUND OF INVENTION
[0003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0004] The major components of every e-commerce are products and consumers that interact with the products. E-commerce deals are consumer-centric which analyse the behaviour of consumers by reviewing history of purchased products. Additionally, e-commerce recommends similar products to consumers having similar purchasing behaviour while estimating the price of a new product, forecasting demands, and finding similar products.
[0005] In today’s digital age, it is easy for customers to identify and compare products across brands, companies, and even marketplaces but it is a difficult task for a company to perform the comparison at scale. Identification of similar products is important, as it can have a significant impact on pricing, catalog management, and other strategic analysis.
[0006] Product matching is very important for any e-commerce marketplace, as it enables recommendation, price comparison, and the like. The challenge in product matching is mostly due to the vastness of product catalog, data heterogeneity, missing product representations, and varying levels of data quality. Moreover, new products are being introduced every day, making it difficult to cast the problem as a classification task.
[0007] However, traditional approaches used for product matching are one dimensional and are based on pre-defined product categorization.
[0008] There is, therefore, a need in the art to provide a system and method that can mitigate the problems associated with the prior art and provide a robust and comprehensive solution for product matching and categorization.

OBJECTS OF THE INVENTION
[0009] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0010] It is an object of the present disclosure to provide a system that uses a tree-based approach for comparing hierarchy of different products.
[0011] It is an object of the present disclosure to provide a system that uses a graph-based approach for comparing named entities for all products and calculates a similarity score.
[0012] It is an object of the present disclosure to provide a system that uses a Regex layer for fetching common attributes between different layers and further assigns categories to all the attributes.
[0013] It is an object of the present disclosure to provide a system that uses artificial intelligence to compare product images and calculate similarity score.
[0014] It is an object of the present disclosure to provide a system that aggregates the similarity score for each feature io obtain a final score.

SUMMARY
[0015] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0016] In an aspect, the present disclosure relates to a system that may include one or more processors operatively coupled to one or more computing devices. The one or more computing devices may be associated with one or more users and may be connected to the one or more processors through a network. The one or more processors may be coupled with a memory that stores instructions to be executed by the one or more processors. The one or more processors may be configured to receive one or more input parameters from the one or more computing devices using one or more catalogue data. The one or more input parameters may be of one or more product inputs provided by the one or more users through the one or more computing devices. Further, the one or more processors may be configured to extract a first set of attributes from the one or more input parameters. The first set of attributes may be indicative of one or more pre-processed data based on the one or more product inputs. Furthermore, the one or more processors may be configured to extract a second set of attributes based on the first set of attributes. The second set of attributes may be indicative of one or more similarities for the one or more pre-processed data. Additionally, the one or more processors may be configured to extract a third set of attributes based on the second set of attributes. The third set of attributes may be indicative of one or more similarity aggregations for the one or more similarities. Based on the first set of attributes, the second set of attributes, and the third set of attributes, the one or more processors may be configured to generate one or more similarity scores for the one or more product inputs using an artificial intelligence (AI) engine. The AI engine may be configured to use one or more techniques. The one or more processors may also be configured to identify one or more similar products based on the one or more similarity scores.
[0017] In an embodiment, the one or more pre-processed data may comprise a product attribute, a product title and description, a product category, and a product image associated with the one or more product inputs.
[0018] In an embodiment, the one or more techniques used by the AI engine may comprise any or a combination of a cosine similarity, a tree-based similarity score computation, a named entity recognition, a graph-based similarity score computation, a regex, and a siamese twins model.
[0019] In an embodiment, the one or more processors may be configured to generate a tree-based similarity score based on the product category for the one or more product inputs.
[0020] In an embodiment, the one or more processors may be configured to generate a graph-based similarity score based on the product title and description for the one or more product inputs.
[0021] In an embodiment, the one or more processors may be configured to generate an attribute-based similarity score based on the product attribute for the one or more product inputs.
[0022] In an embodiment, the one or more processors may be configured to determine one or more similar attributes among the one or more product inputs and assign one or more categories for the one or more product inputs.
[0023] In an embodiment, the one or more processors may be configured to compute a weighted average from the one or more categories for the one or more product inputs. Additionally, the one or more processors may be configured generate the attribute-based similarity score from the weighted average.
[0024] In an embodiment, the one or more processors may be configured to generate an image-based similarity score based on the product image for the one or more product inputs.
[0025] In an aspect, a method for identification of one or more similar products in e-commerce may include receiving, by a processor, one or more input parameters from one or more computing devices using one or more catalogue data. The one or more computing devices may be associated with one or more users and may be connected to the processor through a network. The one or more input parameters may be indicative of one or more product inputs provided by the one or more users through the one or more computing devices. Further, the method may include extracting, by the processor, a first set of attributes from the one or more input parameters. The first set of attributes may be indicative of one or more pre-processed data based on the one or more product inputs. The method may further include extracting, by the processor, a second set of attributes based on the first set of attributes. The second set of attributes may be indicative of one or more similarities of the one or more pre-processed data. Furthermore, the method may include extracting, by the processor, a third set of attributes based on the second set of attributes. The third set of attributes may be indicative of one or more similarity aggregations of the one or more similarities. The method may include generating, by the processor, based on the first set of attributes, the second set of attributes, and the third set of attributes, one or more similarity scores for the one or more product inputs using an AI engine. Additionally, the method may include identifying, by the processor, the one or more similar products in e-commerce based on the one or more similarity scores.
[0026] In an embodiment, the method may include generating, by the processor, a tree-based similarity score based on a product category for the one or more product inputs.
[0027] In an embodiment, the method may include generating, by the processor, a graph-based similarity score based on a product title and description for the one or more product inputs.
[0028] In an embodiment, the method may include generating, by the processor, an attribute-based similarity score based on a product attribute for the one or more product inputs.
[0029] In an embodiment, the method may include generating, by the processor, an image-based similarity score based on a product image for the one or more product inputs.

BRIEF DESCRIPTION OF DRAWINGS
[0030] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0031] FIG. 1 illustrates an exemplary architecture (100) of a proposed system (110), in accordance with an embodiment of the present disclosure.
[0032] FIG. 2 illustrates an exemplary representation (200) of a proposed system (110), in accordance with an embodiment of the present disclosure.
[0033] FIG. 3 illustrates an exemplary block diagram representation (300) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0034] FIG. 4 illustrates an exemplary hierarchical similarity architecture (400) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0035] FIG. 5 illustrates an exemplary title and description similarity architecture (500) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0036] FIG. 6 illustrates an attribute similarity architecture (600) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0037] FIG. 7 illustrates an image similarity architecture (700) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0038] FIGs. 8A-8C illustrate image similarity model training, image similarity model testing, and aggregation (800-1, 800-2, 800-3) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0039] FIG. 9 illustrates an exemplary computer system (900) in which or with which the proposed system (110) may be implemented, in accordance with embodiments of the present disclosure.
[0040] The foregoing shall be more apparent from the following more detailed description of the disclosure.

BRIEF DESCRIPTION OF INVENTION
[0041] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0042] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0043] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0044] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0045] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0046] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0047] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0048] The present disclosure may identify similar products in an e-commerce platform. A similarity score may be computed to distinguish between various products. For example, one or more product inputs may include catalogue data and competitor catalogue data provided by users. Product features such as hierarchy, title, and description, product attributes, and image may be used independently to calculate a similarity score and classify the products as similar/dissimilar.
[0049] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-9.
[0050] FIG. 1 illustrates an exemplary network architecture (100) of a system (110) in accordance with an embodiment of the present disclosure. As illustrated in FIG. 1, a plurality of computing devices (104-1, 104-2…104-N), herein referred as computing devices (104), may be connected to the system (110). The computing devices (104) may also be known as a user equipment (UE) that may include, but not be limited to, a mobile, a laptop, etc. Further, the computing devices (104) may include a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, and mainframe computer. Additionally, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen and the like may be used. It may be appreciated that the user computing devices (104) may not be restricted to the mentioned devices and various other devices may be used. Further, the computing devices (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, a keyboard, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen, and the like. It may be appreciated that the computing devices (104) may not be restricted to the mentioned devices and various other devices may be used.
[0051] Referring to FIG. 1, the computing devices (104) may be connected to the system (110) through a network (106). One or more users (102) (herein referred as users (102)) may provide one or more input parameters indicative of one or more product inputs through the computing devices (104). The system (110) may further include an AI engine (216) for generating one or more similarity scores for the one or more product inputs using one or more techniques. The system (110) may further identify one or more similar products in an e-commerce based on the one or more similarity scores.
[0052] In an embodiment, the computing devices (104) may communicate with the system (110) through a set of executable instructions residing on any operating system including, but not limited to, AndroidTM and the like.
[0053] In an exemplary embodiment, a network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. One or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth may be included by the one or more nodes. A network (106) may include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, and a private network. Further, the network (106) may include a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a public-switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fibre optic network, or some combination thereof.
[0054] FIG. 2 illustrates an exemplary representation (200) of the system (110), in accordance with an embodiment of the present disclosure.
[0055] In an embodiment, the system (110) may comprise one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (110). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[0056] In an embodiment, the system (110) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as input/output (I/O) devices, storage devices, and the like. The interface(s) (206) may facilitate communication through the system (110). The interface(s) (206) may also provide a communication pathway for one or more components of the system (110). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210).
[0057] The processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (110) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (110) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0058] In an embodiment, the system (110) may include one or more processor(s) (202), coupled to a memory (204) with instructions, which when executed causes the one or more processor(s) (202) to receive one or more input parameters from computing devices, such as the computing devices (104) of FIG. 1, using one or more catalogue data. The processing engine (208) may include one or more engines selected from any of a parameter acquisition engine (212), an extraction engine (214), an AI engine (216), and other engine(s) (218).
[0059] In an embodiment, the parameter acquisition engine (212) may receive one or more input parameters from the computing devices (104) using the one or more catalogue data. The one or more input parameters may be indicative of one or more product inputs provided by users, such as users (102) of FIG. 1 through the computing devices (104).
[0060] In an embodiment, the extraction engine (214) may extract a first set of attributes from the one or more input parameters and store the first set of attributes in the database (210). The first set of attributes may be indicative of one or more pre-processed data based on the one or more product inputs. Further, the one or more pre-processed data may comprise a product attribute, a product title and description, a product category, and a product image associated with the one or more product inputs.
[0061] Further, the extraction engine (214) may extract a second set of attributes based on the first set of attributes and store the second set of attributes in the database (210). The second set of attributes may be indicative of one or more similarities for the one or more pre-processed data. Also, the extraction engine (214) may extract a third set of attributes based on the second set of attributes and store the third set of attributes in the database (210). The third set of attributes may be indicative of one or more similarity aggregations for the one or more similarities. Based on the first set of attributes, the second set of attributes, and the third set of attributes, the one or more processor(s) (202) may generate one or more similarity scores for the one or more product inputs using the AI engine (216). The AI engine (216) may be configured to use one or more techniques. The one or more processor(s) (202) may further identify one or more similar products in an e-commerce based on the one or more similarity scores.
[0062] In an embodiment, the one or more processor(s) (202) may generate a tree-based similarity score based on the product category for the one or more product inputs. In an embodiment, the one or more processor(s) (202) may generate a graph-based similarity score based on the product title and description for the one or more product inputs. In another embodiment, the one or more processor(s) (202) may generate an attribute-based similarity score based on the product attribute for the one or more product inputs. In yet another embodiment, the one or more processor(s) (202) may generate an image-based similarity score based on the product image for the one or more product inputs.
[0063] In an embodiment, the one or more processor(s) (202) may determine one or more similar attributes among the one or more product inputs and assign one or more categories for the one or more product inputs.
[0064] In an embodiment, the one or more techniques used by the AI engine (216) may comprise a cosine similarity, a tree-based similarity score computation, a named entity recognition, a graph-based similarity score computation, a Regex, and a Siamese twins model.
[0065] In an embodiment, the other engine(s) (218) may include a data acquisition module, a data preprocessing module, and a similarity computation module (not shown in FIG. 2). Referring to FIG. 3, the data acquisition module (302) module may receive the one or more input parameters and send the one or more parameters to the data preprocessing module (304). The data preprocessing module (304) may pre-process the received one or more input parameters and send the one or more input parameters to the similarity computation module (306). Further, the similarity computation module (306) may calculate similarity for each dimension and generate a weighted feature combinator. Further, the one or more processor(s) (202) may obtain a similarity score based on the weighted feature combinator to identify the one or more similar products in an e-commerce.
[0066] Referring to FIG. 3, an exemplary block diagram representation (300) of the proposed system (110) is shown, in accordance with an embodiment of the present disclosure.
[0067] In an exemplary embodiment, the proposed system (110) as shown in FIG. 1 may receive catalogue data and competitor catalogue data from the data acquisition module (302). The data acquisition module (302) may consist of internal and external data acquisition tasks such as, but not limited to, accessing internal catalogues, scrapping marketplace data, and accessing third party catalogues. The data preprocessing module (304) may consist of all the preprocessing for data from different sources for product attribute, title, description, hierarchy, and images to be input to the respective similarity modules. Further, as described above, the similarity computation module (306) may calculate the similarity for each dimension and generate a weighted feature combinator.
[0068] FIG. 4 illustrates an exemplary hierarchical similarity architecture (400) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0069] In an exemplary embodiment, a hierarchy similarity module (308) may be used to calculate similarity score for the product hierarchy. The product hierarchy may be a categorization tree under which a product is stored. The challenge with similarity in hierarchy is that the categorization can vary in depth and content. Hence, a tree-based similarity score computation may be adopted to overcome the challenges. As shown in FIG. 4, multiple steps may be followed to generate a final similarity score. Product hierarchy may be followed by text preprocessing. The text preprocessing may remove stopwords, remove punctuations, stemming, etc. Further, a tree-based approach may be followed to obtain a similarity calculator layer. Finally, the similarity score may be obtained.
[0070] FIG. 5 illustrates an exemplary title and description similarity architecture (500) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0071] In an exemplary embodiment, a title and description similarity module (310) may be used to compute a similarity score on title and voluminous description of the products. Computing a similarity score only on text without context would not be beneficial. Hence, named entity recognition may be used to extract information from the text and create multi-word hash nodes which may be a part of a graph-based similarity score computation. As shown in FIG. 5, multiple steps may be followed to generate the final similarity score. The product title and description may include text preprocessing. The text pre-processing may remove stopwords, remove punctuations, stemming, etc. Further, information extraction may include a graph-based approach to obtain the similarity score.
[0072] FIG. 6 illustrates an attributes similarity architecture (600) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0073] In an exemplary embodiment, an attributes similarity module (312) may compute similarity score on attributes of a product. The attributes are relatively structured but can widely vary across categories and marketplaces. As shown in FIG. 6, the product attributes may be followed by text pre-processing. The text pre-processing may remove stopwords, remove punctuations, stemming, etc. Further, a Regex filter may be used to find out similar attributes among the product attributes. Further, the product attributes may be categorized into categorical, multi-categorical, dimensional, and numerical categories. Additionally, a similarity calculator layer may be obtained from the dimensional and numerical categories of the product attributes. Also, an embedding layer may be obtained from the categorical and the multi-categorical categories of the product attributes. The embedding layer may be followed by a similarity calculator layer. Further, a weighted average may be computed from the similarity calculator layers to generate the similarity score.
[0074] FIG. 7 illustrates an image similarity architecture (700) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0075] In an exemplary embodiment, an image similarity module (314) may compute a similarity score based on images of products using deep learning. As shown in FIG. 7, product image may be followed by image pre-processing to produce an image similarity model. The image similarity model may generate a similarity score for the product images.
[0076] FIG. 8A illustrates image similarity model training (800-1) for the proposed system (110), in accordance with an embodiment of the present disclosure.
[0077] In an exemplary embodiment, two images (x1, x2) may be provided to identical subnetworks (F1, F2) with shared weights to generate contrastive loss (label) as shown in FIG. 8A.
[0078] FIG. 8B illustrates image similarity model testing (800-2) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0079] Images (s1, s2) are provided to a convolutional neural network (ConvNet) with encodings h (image 1), h (image 2) to generate euclidean distance (h1, h2) to find the similarity score as shown in FIG. 8B.
[0080] FIG. 8C illustrates aggregation (800-3) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0081] In an exemplary embodiment, a similarity aggregator may generate a similarity score for various features of the products. S1 may be a similarity score from the hierarchy/category, while S2 may be a similarity score from title and description. Additionally, S3 may be a similarity score from attributes while S4 may be a similarity from images. Each of the similarity scores may be combined with their corresponding weights for each dimension to generate the similarity score for each feature.
[0082] FIG. 9 illustrates an exemplary computer system (900) in accordance with embodiments of the present disclosure. As shown in FIG. 9, the computer system (900) may include an external storage device (910), a bus (920), a main memory (930), a read-only memory (940), a mass storage device (950), a communication port (960), and a processor (970). A person skilled in the art will appreciate that the computer system (900) may include more than one processor and communication ports. The processor (970) may include various modules associated with embodiments of the present disclosure. The communication port (960) may be any of an RS-252 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port (960) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (900) connects. The main memory (930) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (940) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (970). The mass storage device (950) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage device (950) includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[0083] The bus (920) may communicatively couple the processor(s) (970) with the other memory, storage, and communication blocks. The bus (920) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB, or the like. The bus (920) may further include connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (970) to the computer system (900).
[0084] Optionally, operator and administrative interfaces, e.g., a display, keyboard, and a cursor control device may also be coupled to the bus (920) to support direct operator interaction with the computer system (900). Other operator and administrative interfaces can be provided through network connections connected through the communication port (960). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (900) limit the scope of the present disclosure.
[0085] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.

ADVANTAGES OF THE INVENTION
[0086] The present disclosure provides a system that uses a tree-based approach for comparing hierarchy of different products.
[0087] The present disclosure provides a system that uses a graph-based approach for comparing named entities for all products and calculates a similarity score.
[0088] The present disclosure provides a system that uses a Regex layer for fetching common attributes between different layers and further assigns categories to all the attributes.
[0089] The present disclosure provides a system that uses artificial intelligence to compare product images and calculating similarity score.
[0090] The present disclosure provides a system that aggregates the similarity score for each feature to obtain the final score.
, Claims:1. A system (110) for identification of one or more similar products in e-commerce, said system (110) comprising:
one or more processors (202) operatively coupled to one or more computing devices (104) and with a memory (204), wherein said memory (204) stores instructions which when executed by the one or more processors (202) causes the one or more processors (202) to:
receive one or more input parameters from the one or more computing devices (104) using one or more catalogue data, wherein the one or more computing devices (104) are associated with one or more users (102) and are connected to the one or more processors (202) through a network (106), and
wherein the one or more input parameters are indicative of one or more product inputs provided by the one or more users (102) through the one or more computing devices (104);
extract a first set of attributes from the one or more input parameters, wherein the first set of attributes are indicative of one or more pre-processed data based on the one or more product inputs;
extract a second set of attributes based on the first set of attributes, wherein the second set of attributes are indicative of one or more similarities for the one or more pre-processed data;
extract a third set of attributes based on the second set of attributes, wherein the third set of attributes are indicative of one or more similarity aggregations for the one or more similarities;
based on the first set of attributes, the second set of attributes, and the third set of attributes, generate one or more similarity scores for the one or more product inputs using an artificial intelligence (AI) engine (216), wherein the AI engine (216) is configured to use one or more techniques; and
identify the one or more similar products based on the one or more similarity scores.
2. The system (110) as claimed in claim 1, wherein the one or more pre-processed data comprises a product attribute, a product title and description, a product category, and a product image associated with the one or more product inputs.
3. The system (110) as claimed in claim 1, wherein the one or more techniques used by the AI engine (216) comprises any or a combination of a cosine similarity, a tree-based similarity score computation, a named entity recognition, a graph-based similarity score computation, a Regex, and a Siamese twins model.
4. The system (110) as claimed in claim 2, wherein the one or more processors (202) are configured to generate a tree-based similarity score based on the product category for the one or more product inputs.
5. The system (110) as claimed in claim 2, wherein the one or more processors (202) are configured to generate a graph-based similarity score based on the product title and description for the one or more product inputs.
6. The system (110) as claimed in claim 2, wherein the one or more processors (202) are configured to generate an attribute-based similarity score based on the product attribute for the one or more product inputs.
7. The system (110) as claimed in claim 1, wherein the one or more processors (202) are configured to determine one or more similar attributes among the one or more product inputs and assign one or more categories for the one or more product inputs.
8. The system (110) as claimed in claim 7, wherein the one or more processors (202) are configured to compute a weighted average from the one or more categories for the one or more product inputs; and generate an attribute-based similarity score based on the weighted average.
9. The system (110) as claimed in claim 2, wherein the one or more processors (202) are configured to generate an image-based similarity score based on the product image for the one or more product inputs.
10. A method for identification of one or more similar products in e-commerce, said method comprising:
receiving, by one or more processors (202), one or more input parameters from one or more computing devices (104) using one or more catalogue data,
wherein the one or more input parameters are indicative of one or more product inputs provided by one or more users (102) through the one or more computing devices (104);
extracting, by the one or more processors (202), a first set of attributes from the one or more input parameters, wherein the first set of attributes are indicative of one or more pre-processed data based on the one or more product inputs;
extracting, by the one or more processors (202), a second set of attributes based on the first set of attributes, wherein the second set of attributes are indicative of one or more similarities of the one or more pre-processed data;
extracting, by the one or more processors (202), a third set of attributes based on the second set of attributes, wherein the third set of attributes are indicative of one or more similarity aggregations of the one or more similarities;
generating, by the one or more processors (202), based on the first set of attributes, the second set of attributes, and the third set of attributes, one or more similarity scores for the one or more product inputs using an artificial intelligence (AI) engine (216); and
identifying, by the one or more processors (202), the one or more similar products based on the one or more similarity scores.
11. The method as claimed in claim 10, wherein the one or more pre-processed data comprises a product attribute, a product title and description, a product category, and a product image associated with the one or more product inputs.
12. The method as claimed in claim 11, comprising generating, by the one or more processors (202), a tree-based similarity score based on the product category for the one or more product inputs.
13. The method as claimed in claim 11, comprising generating, by the one or more processors (202), a graph-based similarity score based on the product title and description for the one or more product inputs.
14. The method as claimed in claim 11, comprising generating, by the one or more processors (202), an attribute-based similarity score based on the product attribute for the one or more product inputs.
15. The method as claimed in claim 11, comprising generating, by the one or more processors (202), an image-based similarity score based on the product image for the one or more product inputs.
16. A user equipment (UE) (104) for identification of one or more similar products in e-commerce, said UE (104) comprising:
one or more processors communicatively coupled to a processor (202) comprised in a system (110), the one or more processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes the UE (104) to:
transmit the one or more input parameters to the processor (202) using one or more catalogue data, wherein the UE (104) is associated with one or more users (102) and is connected to the processor (202) of the system (110) through a network (106);
wherein the processor (202) is configured to:
receive the one or more input parameters from the UE (104) using the one or more catalogue data, wherein the one or more input parameters are indicative of one or more product inputs provided by the one or more users (102);
extract a first set of attributes from the one or more input parameters, wherein the first set of attributes are indicative of one or more pre-processed data based on the one or more product inputs;
extract a second set of attributes based on the first set of attributes, wherein the second set of attributes are indicative of one or more similarities of the one or more pre-processed data;
extract a third set of attributes based on the second set of attributes, wherein the third set of attributes are indicative of one or more similarity aggregations of the one or more similarities;
based on the first set of attributes, the second set of attributes, and the third set of attributes, generate one or more similarity scores for the one or more product inputs using an artificial intelligence (AI) engine (216), wherein the AI engine (216) is configured to use one or more techniques; and
identify the one or more similar products based on the one or more similarity scores.