Description:TECHNICAL FIELD
[0001] The present disclosure, in general, relates to listing products on an electronic marketplace, and in particular, relates to approaches for automatically generating and recommending a product for an electronic marketplace based at least on selling score of products.

BACKGROUND
[0002] The following description of the 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 the prior art.
[0003] With the advancement in technology and increasing reliance on digitalization, various products and items may be available on electronic marketplaces. The conventional department stores, to cater to the changing requirements of customers, may also have to move towards bringing their marketplace on a digital platform. As would be understood, with the increase in different electronic marketplaces and different sellers, the competition and the race to provide new products to customers may also increase continuously.
[0004] Even if an administrator of the electronic marketplace comes up with new products, they may not able to diligently check if the new product is already available on the marketplace or if the price of the new product is competitive enough to attract customers or not. Thereafter, to cater to this problem, conventionally, huge investments may need to be made and extensive resources may be deployed to identify items and products that may have a potential to perform well on the electronic marketplace, evaluation of their seller feasibility, identification of the right price, etc.
[0005] In such cases, either the product that has performed well on the competitor platform may be brought on the electronic marketplace and tried. Or, the sellers may be requested to deploy products on the electronic marketplace. In either of the cases, the administrator of the electronic marketplace may be unable to control the products that may be listed on the marketplace. Even further, the process may be manual and heuristics-driven, as a result, being computationally expensive, economically expensive, inefficient, unreliable, and cumbersome. Furthermore, owing to the manual process being solved by multiple resources, the inter-reliability of the resources on each other may also bring an inefficiency.
[0006] Therefore, there exists a need for approaches where appropriate products may be provided efficiently and automatically to the marketplace which may have a good selling potential on the electronic marketplace.

SUMMARY
[0007] This section is provided to introduce certain objects and aspects of the present invention 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. Aspects of the present disclosure relate to listing products on an electronic marketplace. In particular, the present disclosure provides approaches for automatically generating and providing a product recommendation for an electronic marketplace based on selling score of products.
[0008] An embodiment of the present disclosure pertains to a system for automatically generating a product recommendation for an electronic marketplace. The system may include a processor and a recommendation unit coupled to the processor. A set of attributes of one of a plurality of categories of products on the electronic marketplace may be identified. Based on the identified set of attributes, a plurality of combinations of attributes may be generated. Thereafter, at least one of the plurality of combinations of attributes may be selected. The selected combinations of attributes may be novel in view of the set of attributes of available products of the corresponding category on the electronic marketplace. Thereafter, a corresponding image for each of the selected combinations of attributes may be generated. Each of the generated images may correspond to a fresh product, and the fresh product may include a set of corresponding attributes. Thereafter, based on the generated image and the set of corresponding attributes, a sale value of the fresh product may be determined. Based on the fresh product and the corresponding sale value, a selling score corresponding to the fresh product may be determined. Thereafter, a product recommendation may be generated for the electronic marketplace. The generated product recommendation may include the fresh product and at least one of the corresponding sale value, selling score, image, and set of attributes.
[0009] In an aspect, the recommendation unit may determine the selling score of the fresh product based on at least one of the corresponding generated image, the set of corresponding attributes, the corresponding sale value, the corresponding visiting frequency, the corresponding sale frequency, or a combination thereof.
[0010] In another aspect, the set of attributes may include at least one of a price, a brand, a raw material, an average stock unit of the product, or a combination thereof.
[0011] In yet another aspect, the recommendation unit may identify the set of attributes of one of the plurality of categories of products on the electronic marketplace based on a relevance score of each of the attributes with reference to one of the product, the category, the electronic marketplace, or a combination thereof.
[0012] In yet another aspect, the recommendation unit may generate the corresponding image based on the selected combinations of attributes using diffusion models.
[0013] In yet another aspect, the recommendation unit may process the generated image by encoding the generated image into a condensed vector space. Thereafter, the recommendation unit may generate a corresponding processed image.
[0014] In yet another aspect, the recommendation unit may determine the sale value of the fresh product based on the generated image and the corresponding set of attributes using a regression model.
[0015] In yet another aspect, the recommendation unit may determine the sale value of the fresh product based on at least a sale value of a plurality of similar products of the corresponding category available on the electronic marketplace.
[0016] Another embodiment of the present disclosure pertains to a method for automatically generating a product recommendation for an electronic marketplace. The method may include identifying, by a system, a set of attributes of one of a plurality of categories of products on the electronic marketplace. Further, the method may include, based on the identified set of attributes, generating, by the system, a plurality of combinations of attributes. Furthermore, the method may include selecting, by the system, at least one of the plurality of combinations of attributes. The selected combinations of attributes may be novel in view of the set of attributes of available products of the corresponding category on the electronic marketplace. Even further, the method may include generating, by the system, a corresponding image for each of the selected combinations of attributes. Each of the generated images may correspond to a fresh product. The fresh product may include a set of corresponding attributes. Even further, the method may include, based on the generated image and the set of corresponding attributes, determining, by the system, a sale value of the fresh product. Even further, the method may include, based on the fresh product and the corresponding sale value, determining, by the system, a selling score corresponding to the fresh product. Thereafter, the method may include generating, by the system, a product recommendation for the electronic marketplace. The generated product recommendation may include the fresh product and at least one of the corresponding sale value, selling score, image, and set of attributes.
[0017] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0019] FIG. 1 illustrates an exemplary network environment with a system for automatically generating and recommending a product for an electronic marketplace, in accordance with an embodiment of the present disclosure;
[0020] FIG. 2A illustrates an exemplary block diagram representing functional units of the system, in accordance with an embodiment of the present disclosure;
[0021] FIG. 2B illustrates an exemplary process flow diagram for automatically generating and recommending a product for an electronic marketplace, in accordance with an embodiment of the present disclosure;
[0022] FIG. 3 illustrates an exemplary flow diagram representing steps of a method for automatically generating and recommending a product for an electronic marketplace , in accordance with an embodiment of the present disclosure; and
[0023] FIG. 4 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be utilized, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0024] 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.
[0025] 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 invention as set forth.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] As used herein, “'connect”, “configure”, “couple” and its cognate terms, such as “connects”, “connected”, “configured”, and “coupled” may include a physical connection (such as a wired/wireless connection), a logical connection (such as through logical gates of semiconducting device), other suitable connections, or a combination of such connections, as may be obvious to a skilled person.
[0030] As used herein, “send”, “transfer”, “transmit”, and their cognate terms like “sending”, “sent”, “transferring”, “transmitting”, “transferred”, “transmitted”, etc. include sending or transporting data or information from one unit or component to another unit or component, wherein the content may or may not be modified before or after sending, transferring, transmitting.
[0031] 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 invention. 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.
[0032] 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.
[0033] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such details as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosures as defined by the appended claims.
[0034] The approaches of the present subject matter provide a robust and an efficient way to automatically generate a product and/or a recommendation of the product for an electronic marketplace based on selling score/potential of products. As would be appreciated, the proposed approach may automatically identify the products which may not be available on the marketplace, and may generate corresponding products along with images for such missing products. As a result, a product recommendation may be automatically generated and, new and fresh products may be provided to a user of the electronic marketplace.
[0035] As would be further appreciated, the proposed approach may also allow to select a competitive and appropriate sale value of each of the fresh products. The selling score of each of the products may also be determined, which may allow an administrator of the electronic marketplace to easily list the products on the electronic marketplace that may have a high selling potential on the marketplace. As would be furthermore appreciated, the proposed approach may be implemented and scalable to multiple businesses in enhancing the sale and growth of various products on various marketplaces.
[0036] The manner in which the proposed system is used for automatically generating and providing a product recommendation for an electronic marketplace based on selling score of products is further explained in detail with respect to FIGs. 1-4. It is to be noted that drawings of the present subject matter shown here are for illustrative purposes only and are not to be construed as limiting the scope of the subject matter claimed. Further, FIGs. 1-2 have been explained together, and same reference numerals have been used to refer to identical components and entities.
[0037] FIG. 1 illustrates an exemplary network environment 100 including a system 102. The system 102 may be used for automatically generating and recommending a product for an electronic marketplace, in accordance with an embodiment of the present disclosure. In one example, the system 102 may be implemented as any hardware-based, software-based, or network-based computing device known to a person skilled in the art. Such explanation has not been provided here for the sake of brevity. It may be further noted that the system 102 may be implemented as any system capable of receiving an input, processing it, and generating an output.
[0038] Continuing further, as depicted in FIG. 1, the network environment 100 may include a centralized server 104 in communication with the system 102 over a network 106. In one example, the centralized server 104 may be implemented using any or a combination of hardware-based, software-based, network-based computing device, or a cloud-based computing device.
[0039] In one example, the network 106 may be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the network 106 may either be a dedicated network or a shared network. The shared network may represent an association of different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[0040] Referring to FIG. 1, the centralized server 104 may include a recommendation unit 108. Although, as depicted in FIG. 1, the recommendation unit 108 may be present within the centralized server 104 and may be in communication with the system 102 over the network 106. However, the same should not be construed to limit the scope of the present subject matter in any manner. The recommendation unit 108 may be present within the system 102 as well, as would be explained with reference to FIG. 2A. Such an example would also lie within the scope of the present subject matter.
[0041] In one example, the recommendation unit 108 may be implemented as a processing resource. In another example, the recommendation unit 108 may be implemented as a combination of a transceiver and a processing resource. The recommendation unit 108 may be capable of receiving data, processing it, and transmitting data.
[0042] A person of ordinary skill in the art will appreciate that the network environment 100 may be modular and flexible to accommodate any kind of changes in the network environment 100.
[0043] The working of the system 102 is explained in conjunction with FIGs. 2A-2B. FIG. 2A illustrates a block diagram representing functional units of the proposed system for automatically generating and recommending a product for an electronic marketplace, such as system 102. Further, FIG. 2B illustrates an exemplary process flow diagram for automatically generating and recommending a product for an electronic marketplace. The process flow diagram may be explained and understood with respect to the exemplary system 102.
[0044] As depicted in FIG. 2A, the exemplary functional units of the system 102 may include 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 manipulate 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 102. The memory 204 may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as Random-access Memory (RAM), or non-volatile memory such as Electrically Erasable Programmable Read-only Memory (EPROM), flash memory, and the like.
[0045] In an embodiment, the system 102 may also include an interface(s) 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the system 102 with various devices coupled to the system 102. The interface(s) 206 may also provide a communication pathway for one or more components of the system 102. Examples of such components include, but are not limited to, processing engine(s) 208 and database 210.
[0046] In an embodiment, 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 include a processing resource (for example, one or more processors), to execute such instructions.
[0047] 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 102 may include 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 102 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry. In an embodiment, the database 210 may include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208. In an embodiment, the processing engine(s) 208 may include a recommendation unit 108 and other unit(s) 212. The other unit(s) 212 may implement functionalities that supplement applications or functions performed by the system 102 and/or the processing engine(s) 208.
[0048] Continuing with the approaches of the working of the present subject matter, in one example, the system 102 may be communicatively coupled to, or implemented in an electronic marketplace. The electronic marketplace, in one example, may be understood as an e-commerce website. In another example, the electronic marketplace may be understood and implemented as an e-commerce mobile application. Such e-commerce platform may be managed by an administrator and may be used by multiple users through their respective user devices. However, any other variation of the electronic marketplace to which or within which the system 102 may be communicatively coupled or implemented may also be possible without deviating from the scope of the present subject matter.
[0049] In another example, the system 102 may be implemented for generating a product recommendation for any other departmental store as well. In such cases, the system 102 may be implemented for managing the products in the departmental store, and automatically generating product recommendations that may include products having a good selling potential. Such products may then be put to display and sold in such departmental stores. However, it may be again noted that all such examples are only illustrative, and any other examples may also be possible without deviating from the scope of the present subject matter.
[0050] In one example, as would be understood, the electronic marketplace may include a plurality of products. Such multiple products may pertain to multiple categories. This step has been depicted as block 214, in one implementation, in FIG. 2B. Examples of such categories may include, but are not limited to, fashion, grocery, and electronics.
[0051] In operation, the recommendation unit 108 may identify a set of attributes of one of a plurality of categories of products on the electronic marketplace. This step has been depicted as block 216, in one implementation, in FIG. 2B. As would be understood, each category of products on the marketplace may include some respective attributes, which may determine the characteristics of the available products of that corresponding marketplace. In one example, the set of attributes of one of the plurality of categories of products on the electronic marketplace that may be identified by the recommendation unit 108 may include, but are not limited to, a price, a brand, a raw material, and an average stock unit of the product. However, it may be noted that all such categories and attributes are only exemplary, and the recommendation unit 108 may identify any other set of attributes of any categories of products on the electronic marketplace. All examples would lie within the scope of the present subject matter.
[0052] In another example, the recommendation unit 108 may identify the set of attributes of one of the plurality of categories of products on the electronic marketplace based on a relevance score of each of the attributes with reference to one of the product, the category, the electronic marketplace, or a combination thereof. The relevance score of each of the attributes may indicate the level of relevance of such attributes with respect to various parameters. The attributes with high relevance score may be identified, while the attributes with low relevance score may be omitted. As would be understood, such method of identification may ensure that most relevant attributes are identified for each of the plurality of categories of products on the marketplace.
[0053] Returning to the present example, the recommendation unit 108, based on the identified set of attributes, may generate a plurality of combinations of attributes. This step has been depicted as block 218, in one implementation, in FIG. 2B. Such generation may be done using any technique well understood to a person skilled in the art. Further, as would be understood, each of the plurality of generated combinations of attributes may correspond to a virtual product that may be listed on the electronic marketplace. Each of the plurality of virtual products may include attributes that may correspond to at least one of the plurality of identified attributes.
[0054] Continuing further, the recommendation unit 108 may then select at least one of the plurality of combinations of attributes. The selected combinations of attributes may be novel in view of the set of attributes of available products on the corresponding category on the electronic marketplace. This step has been depicted as block 220, in one implementation, in FIG. 2B. As described previously, each of the plurality of generated combinations of attributes correspond to a virtual product. Therefore, the recommendation unit 108, by selecting at least one of the plurality of combinations of attributes, may select at least one of the plurality of generated virtual products. The attributes of such selected virtual products may be novel with respect to the attributes of products those already available on the electronic marketplace. As a result, such selection may provide at least one of a plurality of virtual products with attributes that are not available on the electronic marketplace.
[0055] Continuing further, the recommendation unit 108 may then generate a corresponding image for each of the selected combinations of attributes. Each of the generated images may correspond to a fresh product. Further, the fresh product may include the set of corresponding attributes. In one example, the recommendation unit 108 may generate the corresponding image based on the selected combinations of attributes using diffusion models. In another example, the recommendation unit 108 may generate the corresponding image for each of the selected combinations of attributes using a “text-to-image” module known to a person skilled in the art. Such module may generate a realistic fresh product image based on the selected combinations of attributes. This step has been depicted as block 222, in one implementation, in FIG. 2B. In yet another example, the text of the selected combinations of attributes may be encoded using a contrastive language-image pre-training (CLIP) text encoder. The image may be encoded using a variational auto encoder (VAE) and a backbone convolution network used for de-noising diffusion may be a U-Net architecture. The text encodings may then be fed into the U-Net via a cross attention mechanism. This step has been depicted as block 224, in one implementation, in FIG. 2B. In yet another example, the U-Net architecture may be fine-tuned for a few epochs on custom dataset for optimal results in terms of Fréchet inception distance (FID) for the generation of image for the selected combinations of attributes. However, it may be noted that such example is only illustrative, and any other method known to a person skilled in the art may also be used to generate the corresponding image. All such examples would lie within the scope of the present subject matter.
[0056] In another example, the recommendation unit 108 may process the generated image by encoding the generated image into a condensed vector space. Thereafter, a corresponding processed image may be generated. In one example, an application programming interface (API) may be used to perform such processing. In such case, the API may expose the inference endpoint of a deep learning Siamese network model trained on product similarity tasks, thus allowing similar products to be represented closer to each other in the embedding space. However, it may be again noted that all such examples are only illustrative, and any other techniques known to a person skilled in the art may also be used without deviating from the scope of the present subject matter.
[0057] Continuing further, based on the generated image and the set of corresponding attributes, the recommendation unit 108 may determine a sale value of the fresh product. This step has been depicted as block 226, in one implementation, in FIG. 2B. In one example, the recommendation unit 108 may determine the sale value of the fresh product using a regression model. In another example, boosting techniques such as CatBoost® may be used to solve the regression task. In yet another example, standard metrics such as mean squared error, mean absolute percentage error, and R_square may be used to determine the sale value of the fresh product. In yet another example, the recommendation unit 108 may determine the sale value of the fresh product using a machine learning (ML) model. In such cases, while training the model, the set of corresponding attributes during the training, validation, and testing phase may be disjoint to ensure generalizability of the determined sale value of the fresh products.
[0058] In yet another example, the recommendation unit 108 may determine the sale value of the fresh product based on at least a sale value of a plurality of similar products of the corresponding category available on the electronic marketplace. In such cases, in the example of ML techniques and models, sample weights may be made dependent on the plurality of similar products of the corresponding category available on the electronic marketplace during the training phase. As would be appreciated, such way of determination may ensure that the determined sale value of the fresh product is competitive and should be easily accepted by the customer/user. However, it may be noted that such example is only illustrative, and any other technique known to a person skilled in the art may also be used to determine the sale value of the fresh products based on the generated image and the corresponding set of attributes. All such examples would lie within the scope of the present subject matter.
[0059] Continuing further with the present example, thereafter, based on the fresh product and the corresponding sale value, the recommendation unit 108 may determine a selling score corresponding to the fresh product. This step has been depicted as block 228, in one implementation, in FIG. 2B. In one example, the recommendation unit 108 may determine the selling score based on at least one of the corresponding generated image, the set of corresponding attributes, the corresponding sale value, the corresponding visiting frequency, the corresponding sale frequency, or a combination thereof. In another example, the recommendation unit 108 may determine the selling score corresponding to the fresh product using an ML model. However, it may be noted that all such examples are only illustrative, and any other parameters may also determine the selling score of the fresh product. All such examples would be covered within the scope of the present subject matter.
[0060] In another example, the selling score of each of the fresh products may be provided in the form of a rank. In another example, the selling score of each of the fresh products may be referred to as the selling potential of the fresh product.
[0061] Continuing further, the recommendation unit 108 may thereafter generate a product recommendation for the electronic marketplace. This step has been depicted as block 230, in one implementation, in FIG. 2B. The generated product recommendation may include the fresh product and at least one of the corresponding sale value, the selling score, image, and the set of attributes. In one example, the generated product recommendation may be provided to the administrator of the electronic marketplace in the form of a report. Based on such report, the administrator may list the products on the electronic marketplace.
[0062] As would be again appreciated, the product recommendation which may be generated and provided to the electronic marketplace may allow such fresh and new products to be listed on the marketplace which have a high selling potential. Not only such products may be new as compared to the available products on the marketplace, but also the sale value of such new products may also be competitive.
[0063] FIG. 3 illustrates an exemplary flow diagram representing steps of a method 300 for automatically generating and providing a product recommendation for an electronic marketplace based on selling score of products, in accordance with an embodiment of the present disclosure. The method 300 may be implemented within the system 102, as described in conjunction with FIGs. 1-2.
[0064] At block 302, the method 300 may include identifying a set of attributes of one of a plurality of categories of products on the electronic marketplace. At block 304, the method 300 may include, based on the identified set of attributes, generating a plurality of combinations of attributes. At block 306, the method 300 may include selecting at least one of the plurality of combinations of attributes. The selected combinations of attributes may be novel in view of the set of attributes of available products of the corresponding category on the electronic marketplace.
[0065] At block 308, the method 300 may include generating a corresponding image for each of the selected combinations of attributes. Each of the generated images may correspond to a fresh product. The fresh product may include a set of corresponding attributes. At block 310, the method 300 may include, based on the generated image and the set of corresponding attributes, determining a sale value of the fresh product. At block 312, the method 300 may include, based on the fresh product and the corresponding sale value, determining a selling score corresponding to the fresh product. At block 314, the method 300 may include generating a product recommendation for the electronic marketplace. The generated product recommendation may include the fresh product and at least one of the corresponding sale value, selling score, image, and set of attributes.
[0066] It may be appreciated that the steps shown in FIG. 3 are merely illustrative. Other suitable steps may be used for the same, if desired. Moreover, the steps of the method 300 may be performed in any order and may include additional steps.
[0067] FIG. 4 illustrates an exemplary computer system 400 in which or with which embodiments of the present disclosure may be utilized. The computing system 400 may be implemented as or within the system 102 described in conjunction with FIGs. 1-2. As depicted in FIG. 4, the computer system 400 may include an external storage device 410, a bus 420, a main memory 430, a read-only memory 440, a mass storage device 450, communication port(s) 460, and a processor 470. A person skilled in the art will appreciate that the computer system 400 may include more than one processor 470 and communication ports 460. The processor 470 may include various modules associated with embodiments of the present disclosure. The communication port(s) 460 may be any of an RS-232 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(s) 460 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 400 connects.
[0068] In an embodiment, the main memory 430 may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory 440 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 470. The mass storage device 450 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are 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).
[0069] In an embodiment, the bus 420 communicatively couples the processor 470 with the other memory, storage, and communication blocks. The bus 420 may be, e.g. a Peripheral Component Interconnect PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), Universal Serial Bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 470 to the computer system 400.
[0070] In another embodiment, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to the bus 420 to support direct operator interaction with the computer system 400. Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) 460. Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system 400 limit the scope of the present disclosure.
[0071] Thus, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named.
[0072] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.
, Claims:1. A system (102) for automatically generating a product recommendation for an electronic marketplace, the system (102) comprising:
a processor;
a recommendation unit (108) coupled to the processor, wherein the recommendation unit (108) is to:
identify a set of attributes of one of a plurality of categories of products on the electronic marketplace;
based on the identified set of attributes, generate a plurality of combinations of attributes;
select at least one of the plurality of combinations of attributes, wherein the selected combinations of attributes are novel in view of the set of attributes of available products of the corresponding category on the electronic marketplace;
generate a corresponding image for each of the selected combinations of attributes, wherein each of the generated images corresponds to a fresh product, and wherein the fresh product comprises a set of corresponding attributes;
based on the generated product and the set of corresponding attributes, determine a sale value of the fresh product;
based on the fresh product and the corresponding sale value, determine a selling score corresponding to the fresh product; and
generate a product recommendation for the electronic marketplace, wherein the generated product recommendation comprises the fresh product and at least one of: the corresponding sale value, selling score, image, and set of attributes.
2. The system (102) as claimed in claim 1, wherein the recommendation unit (108) is to determine the selling score of the fresh product based on at least one of: the corresponding generated image, the set of corresponding attributes, the corresponding sale value, the corresponding visiting frequency, the corresponding sale frequency, or a combination thereof.
3. The system (102) as claimed in claim 1, wherein the set of attributes comprises at least one of: a price, a brand, a raw material, an average stock unit of the product, or a combination thereof.
4. The system (102) as claimed in claim 1, wherein the recommendation unit (108) is to identify the set of attributes of one of the plurality of categories of products on the electronic marketplace based on a relevance score of each of the attributes with reference to one of: the product, the category, the electronic marketplace, or a combination thereof.
5. The system (102) as claimed in claim 1, wherein the recommendation unit (108) is to generate the corresponding image based on the selected combinations of attributes using diffusion models.
6. The system (102) as claimed in claim 1, wherein the recommendation unit (108) is to:
process the generated image by encoding the generated image into a condensed vector space; and
generate a corresponding processed image.
7. The system (102) as claimed in claim 1, wherein the recommendation unit (108) is to determine the sale value of the fresh product based on the generated image and the corresponding set of attributes using a regression model.
8. The system (102) as claimed in claim 1, wherein the recommendation unit (108) is to determine the sale value of the fresh product based on at least a sale value of a plurality of similar products of the corresponding category available on the electronic marketplace.
9. A method (300) for automatically generating a product recommendation for an electronic marketplace, the method (300) comprising:
identifying (302), by a system (102), a set of attributes of one of a plurality of categories of products on the electronic marketplace;
based on the identified set of attributes, generating (304), by the system (102), a plurality of combinations of attributes;
selecting (306), by the system (102), at least one of the plurality of combinations of attributes, wherein the selected combinations of attributes are novel in view of the set of attributes of available products of the corresponding category on the electronic marketplace;
generating (308), by the system (102), a corresponding image for each of the selected combinations of attributes, wherein each of the generated images corresponds to a fresh product, and wherein the fresh product comprises a set of corresponding attributes;
based on the generated image and the set of corresponding attributes, determining (310), by the system (102), a sale value of the fresh product;
based on the fresh product and the corresponding sale value, determining (312), by the system (102), a selling score corresponding to the fresh product; and
generating (314), by the system (102), a product recommendation for the electronic marketplace, wherein the generated product recommendation comprises the fresh product and at least one of: the corresponding sale value, selling score, image, and set of attributes.