Method for operating a platform by supporting gamification based on consumption similarity and computing device for performing the same

Abstract

A computing device that supports gamification based on consumption similarity, performed by a computing device comprising at least one processor according to some embodiments of the present disclosure, is disclosed. The computing device may include a parameter definition unit that defines a first parameter and a second parameter; a purchase propensity determination unit that determines the purchase propensity of a plurality of users; a grouping unit that determines a first group; a user interface unit that provides a user interface that supports gamification; and a mission determination unit that determines at least one mission to induce a purchase.

Description

Technology Field

[0001] The present disclosure relates to a method of operating a platform by supporting gamification, and specifically to a method of operating a platform by supporting gamification based on consumption similarity. Background Technology

[0002] The Korean population in North America is estimated to be over 2.5 million, possessing an annual purchasing power of approximately $5 billion. In particular, demand for traditional foods, beauty products, and household goods accounts for more than 60% of total purchasing power.

[0003] Existing global platforms have a limited range of Korean products. For example, traditional Korean foods account for less than 10% of all products registered on the platform. Furthermore, due to a lack of localization services, the average delivery time takes over two weeks, and shipping costs range from $50 to $100. Consequently, consumer satisfaction is inevitably low.

[0004] More than 70% of North American consumers place high trust in Korean products, and there is a growing demand for specialized platforms with localized services and competitive prices.

[0005] However, even if a platform offers localized services and competitive prices, if it fails to identify users' purchasing tendencies or consumption habits and thus fails to attract their interest in the product, it may be difficult to achieve actual sales. Prior art literature

[0006] Republic of Korea Published Patent No. 10-2019-0033885 The problem to be solved

[0007] The present disclosure is conceived in response to the aforementioned background technology and aims to provide a method for operating a platform by supporting gamification based on consumption similarity, and a computing device for performing the method.

[0008] The technical problems of the present disclosure are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0009] According to one embodiment of the present disclosure for solving the problem described above, a method for supporting gamification based on consumption similarity is disclosed, which is performed by a computing device comprising at least one processor. The method for supporting gamification based on consumption similarity comprises: defining a first parameter for each of a plurality of products based on product information for a plurality of products and purchase records of a plurality of users associated with the plurality of products; determining a purchase propensity of a plurality of users based on an interaction from a plurality of users selecting at least one product among the plurality of products and user information associated with the plurality of users; defining a second parameter for each of the plurality of users based on the first parameter, the user information, and the purchase propensity; determining at least one second user in which a second-2 parameter having a similarity of at least a predetermined threshold with a second-1 parameter of a first user appears; determining a first group that groups the first user and the at least one second user; and providing a user interface that supports gamification associated with the first group, wherein the user interface includes a percentage gauge associated with the first user's mission achievement. The method may include the step of determining at least one mission to induce a purchase by the first user—the at least one mission is determined based on the first parameter and the second parameter—; the step of providing the determined at least one mission through the user interface; and the step of updating the progress of the percentage gauge according to the achievement of the at least one mission.

[0010] In addition, the above first parameter is a mathematical formula Determined based on, and the above silver Indicates the category of the nth product, and the above The above Indicates the price range of the th product and the above The above Indicates the main purchasing age group of the th product, and the above The above It can indicate the main purchasing gender of the th product.

[0011] In addition, the above similarity is a mathematical formula Determined based on, and the above represents the 2-1 parameter related to the above-mentioned first user, and the above represents a second-2 parameter related to at least one second user, and the represents the total number of unique parameters included in the above 2-1 parameter and the above 2-2 parameter, and the can represent the number of matching parameters between the above 2-1 parameter and the above 2-2 parameter.

[0012] Additionally, the method may further include the step of determining a reward to be paid to the first user based on the progress of the percentage gauge; and the step of reflecting the reward in payment according to the user interaction of the first user who purchases a product related to the achievement of at least one mission.

[0013] Additionally, a computing device that supports gamification based on consumption similarity is disclosed. The computing device comprises: a parameter definition unit that defines a first parameter for each of the plurality of products based on product information for a plurality of products and purchase records of a plurality of users related to the plurality of products, and defines a second parameter for each of the plurality of users based on the first parameter, user information related to the plurality of users, and the purchasing propensity of the plurality of users; a purchasing propensity determination unit that determines the purchasing propensity of the plurality of users based on an interaction from the plurality of users selecting at least one product among the plurality of products and user information related to the plurality of users; and a grouping unit that determines at least one second user in which a second-2 parameter having a similarity of at least a predetermined threshold with a second-1 parameter of the first user appears, and determines a first group formed by grouping the first user and the at least one second user. The present invention may include a user interface that supports gamification related to the first group, wherein the user interface includes a percentage gauge related to the achievement of the first user's mission, and provides at least one mission through the user interface, and updates the progress of the percentage gauge according to the achievement of the at least one mission; and a mission determination unit that determines at least one mission to induce the first user to make a purchase, wherein the at least one mission is determined based on the first parameter and the second parameter.

[0014] The technical solutions obtainable in this disclosure are not limited to the solutions mentioned above, and other solutions not mentioned will be clearly understood by those skilled in the art to which this disclosure belongs from the description below. Effects of the invention

[0015] According to some embodiments of the present disclosure, a method and a computing device that support gamification based on consumption similarity, which can induce consumer participation and increase purchase conversion rates, can be provided.

[0016] The effects obtainable from the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art to which the present disclosure belongs from the description below. Brief explanation of the drawing

[0017] Various aspects are now described with reference to the drawings, wherein similar reference numbers are used to collectively refer to similar components. In the following embodiments, for illustrative purposes, a number of specific details are presented to provide a comprehensive understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details. In other examples, known structures and devices are illustrated in block diagram form to facilitate the description of one or more aspects. FIG. 1 is a block diagram illustrating an example of a computing device according to some embodiments of the present disclosure. FIG. 2 is a flowchart illustrating an example of a method in which a computing device according to some embodiments of the present disclosure supports gamification based on consumption similarity. FIG. 3 is a drawing for illustrating an example of a user interface provided through a user interface unit according to some embodiments of the present disclosure. FIG. 4 is a drawing for illustrating another example of a user interface provided through a user interface section according to some embodiments of the present disclosure. Specific details for implementing the invention

[0018] The present invention is susceptible to various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0019] Terms such as first, second, A, B, etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0020] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0021] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0022] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0023] In the present disclosure, a computing device may support gamification based on consumption similarity. Gamification is a compound word of "game" and "~ification," and can be understood as a technique that provides users with the enjoyment of purchasing by applying game mechanisms, ways of thinking, design elements, etc. Supporting gamification can be understood as supporting users to feel enjoyment in purchasing.

[0024] For example, the computing device may include a user interface that includes a percentage gauge related to the achievement of a mission. The mission may be, for example, an action of adding a predetermined product to a shopping cart, an action of adding a set number of products to a shopping cart, or an action of adding a product associated with a specific product to a shopping cart. The computing device (100) may update the progress of the percentage gauge according to the user's mission achievement level. Depending on the user's mission achievement level, the computing device (100) may also output messages through the user interface such as, "People similar to you have purchased ~~ in the ~~ field. Would you like to purchase? Mission accomplished!" or "~~~ remaining to reach the TOP 5 monthly purchases for people in their 30s! Upon reaching the TOP 5, a ~~~ coupon will be presented!" By supporting such gamification that stimulates the user's interest, the computing device (100) can induce the user to make a purchase. Hereinafter, an example of a method in which the computing device (100) according to the present disclosure supports gamification based on consumption similarity will be described with reference to FIGS. 1 to 4.

[0025] FIG. 1 is a block diagram illustrating an example of a computing device according to some embodiments of the present disclosure.

[0026] Referring to FIG. 1, the computing device (100) may include a parameter definition unit (110), a purchase propensity determination unit (120), a grouping unit (130), a user interface unit (140), a mission determination unit (150), a reward payment unit (160), a storage unit (170), a communication unit (180), and a control unit (190). However, the above-described components are not essential for implementing the computing device (100), so the computing device (100) may have more or fewer components than the components listed above.

[0027] The computing device (100) may include any type of computer system or computer device, such as, for example, a microprocessor, a mainframe computer, a digital processor, a portable device, or a device controller.

[0028] A computing device (100) may achieve desired system performance by utilizing a combination of typical computer hardware (e.g., a device that may include a computer processor, memory, storage, input and output devices, and other components of a conventional computing device; electronic communication devices such as routers and switches; and electronic information storage systems such as network-attached storage (NAS) and storage area network (SAN)) and computer software (i.e., instructions that cause the computing device to function in a specific way). The computing device (100) may include a personal computer (PC), notebook, mobile terminal, smartphone, tablet PC, etc. owned by the user, and may include all types of terminals capable of connecting to a wired or wireless network.

[0029] The parameter definition unit (110) can organize the parameters. The parameter definition unit (110) can define the first parameter and the second parameter.

[0030] The first parameter may be a parameter for each of the multiple products. The parameter definition unit (110) may define the first parameter for each of the multiple products based on information about the multiple products and purchase records of users related to the multiple products. The purchase records may include the age of the user who purchased the product, the gender of the user who purchased the product, the date the product was sold, or the time the product was sold. The purchase records may be understood as records regarding the products.

[0031] The second parameter may be a parameter for each of multiple users.

[0032] The parameter definition unit (110) can define a second parameter for each of a plurality of users based on the first parameter, user information, and purchasing tendency.

[0033] The purchasing propensity determining unit (120) can determine the purchasing propensity of multiple users.

[0034] The grouping unit (130) can group users based on each of the second parameters of multiple users.

[0035] The user interface section (140) can provide a user interface that supports gamification. Gamification is a compound word of game and ~ification, and can be understood as a technique that provides the user with the fun of purchasing by applying game mechanisms, ways of thinking, design elements, etc. Through the user interface that supports gamification, the user can feel interest such as competing with other users or performing missions. Accordingly, the user's desire to purchase can be enhanced.

[0036] The mission determination unit (150) can determine a mission. The mission may be provided through a user interface. The mission may be a tool to enhance the user's desire to purchase or to stimulate interest.

[0037] The reward payment unit (160) can determine the reward to be paid to the user based on the progress of the percentage gauge. The reward payment unit (160) can reflect the reward in the payment according to the user interaction of actually purchasing the product.

[0038] The storage unit (170) may include memory and / or a permanent storage medium. The memory may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk.

[0039] In the present disclosure, the storage unit (170) may store product information for a plurality of products, purchase records of users for each of the plurality of products, and user information related to the user.

[0040] The communication unit (180) may include one or more modules that enable communication between the computing device (100) and the communication system, between the computing device (100) and the user terminal, or between the computing device (100) and the network.

[0041] The control unit (190) can typically handle the overall operation of the computing device (100). The control unit (190) can control the operation of the previously described parameter definition unit (110), purchase propensity determination unit (120), grouping unit (130), user interface unit (140), mission determination unit (150), reward payment unit (160), storage unit (170), and communication unit (180). The control unit (190) can provide or process appropriate information or functions to the user by processing signals, data, information, etc. that are input or output through the components of the computing device (100), or by running an application program stored in the storage unit (170).

[0042] The control unit (190) may be composed of one or more cores and may include a processor for data analysis such as a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), or a tensor processing unit (TPU).

[0043] Below, an example of a method in which a computing device (100) supports gamification based on consumption similarity is described.

[0044] FIG. 2 is a flowchart illustrating an example of a method in which a computing device according to some embodiments of the present disclosure supports gamification based on consumption similarity.

[0045] Referring to FIG. 2, the parameter definition unit (110) of the computing device (100) can define a first parameter for each of the plurality of products based on product information for the plurality of products and purchase records of the plurality of users related to the plurality of products (S110).

[0046] Information regarding a product may include the product name, product category, product price, product price range, product material, product use, and product expiration date. Purchase records can be understood as records of products being sold. Purchase records may include information related to which user the product was sold to, for how much it was sold, or when it was sold.

[0047] According to one embodiment, the first parameter can be determined based on the following mathematical formula.

[0048]

[0049] Is It can be the first parameter for the nth product. is a natural number. silver It can represent the category of the nth product. It can represent categories of products such as daily necessities, electronic devices, clothing, or food. Is It can represent the price range of the nth product. It can represent the price range of products, such as low, mid, or high. and Is It can be determined based on product information for the th product. Is It can indicate the main purchasing age group of the th product. It can represent major purchasing age groups such as teenagers, 20s, 30s, 40s, or 50s. Is It can indicate the main purchasing gender of the th product. It can indicate the primary purchasing gender, such as male or female. and can be determined based on purchase records. The parameter definition unit (110) is Regarding the third product A first parameter such as can be defined. The first parameter is the above-described inside In addition, other detailed parameters may be included.

[0050] The purchasing propensity determination unit (120) can determine the purchasing propensity of multiple users based on the interaction from multiple users selecting at least one product among multiple products and user information related to multiple users (S120).

[0051] User information may be information related to a user. User information may include identification information for identifying the user, the user's age, the user's gender, the user's address, the user's phone number, a list of products purchased by the user, costs incurred by the user on the platform, the user's purchase history, the number of purchases made by the user, the user's purchase frequency, the user's visit history to the platform, the connection status of the platform, or the user's rank within the platform. The purchase propensity determination unit (120) may determine the purchase propensity of each of the multiple users based on user information related to each of the multiple users.

[0052] When a user visits the platform for the first time, there may not be any records related to the user's purchase. When a user visits the platform for the first time, the purchase propensity determination unit (120) may provide multiple products through the user interface unit (140). The purchase propensity determination unit (120) may determine multiple products to be provided through the user interface based on age or gender, etc., included in the user information. The purchase propensity determination unit (120) may determine the purchase propensity of multiple users based on user interaction in which at least one product among the multiple products is selected.

[0053] According to one embodiment, the purchase propensity determination unit (120) can determine the purchase propensity of a plurality of users through a pre-trained first neural network model. The first neural network model may be a model trained to determine the purchase propensity by using learning user information related to a plurality of learning users and interactions from a plurality of learning users. The purchase propensity determination unit (120) can determine the purchase propensity of a plurality of users by inputting user information related to a plurality of users and interactions from a plurality of users selecting at least one product among a plurality of products into the first neural network model.

[0054] The first neural network model may include sLLM (small Large Language Model), SLM (Small Language Model), Large Language Model (LLM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), autoencoder, Generative Adversarial Networks (GAN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Q Network, U Network, Siamese Network, or Generative Adversarial Network (GAN).

[0055] The control unit (190) can perform operations for learning the first neural network model.

[0056] The control unit (190) can perform one or more of the operations of processing input data for learning in deep learning, extracting features from input data, and updating the weights of a neural network using backpropagation. The control unit (190) can perform learning of a network function using at least one of a central processing unit, a general-purpose graphics processing unit, or a tensor processing unit. For example, the central processing unit and the general-purpose graphics processing unit can perform operations for learning a network function or operations for processing data classification using a network function.

[0057] A neural network can be composed of a set of nodes. A node can be understood as a set of interconnected computational units. Alternatively, a neural network may be composed of a set of neurons. The nodes (or neurons) constituting a neural network may be interconnected by one or more links.

[0058] In a neural network, one or more nodes connected via links can form input and output node relationships. Any node in an output node relationship with respect to a given node may be in an input node relationship with respect to another node. Any node in an input node relationship with respect to a given node may be in an output node relationship with respect to another node. One or more output nodes may be connected to a single input node via links, and one or more input nodes may be connected to a single output node via links.

[0059] The relationship between input and output nodes can be determined during the learning process based on links. In the relationship between input and output nodes connected through a single link, the data of the output node can be determined based on the data input to the input node. The links interconnecting the input and output nodes may have weights. Weights can be variable and may be varied by a user or an algorithm, etc., to perform the functions required by the first neural network model. For example, the output node value can be determined based on the value input to at least one input node connected to the output node and the weight set on the link corresponding to each input node. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the relationships between the nodes and links, and the values ​​of the weights assigned to each link. For example, if two neural networks exist with the same number of nodes and links but different link weight values, the two neural networks can be recognized as different neural networks.

[0060] A subset of nodes constituting a neural network can form a layer. Some of the nodes constituting the neural network may form a layer based on distances from the initial input node. The distance from the initial input node can be defined by the minimum number of links that must be traversed to reach the corresponding node from the initial input node. Here, the initial input node can be understood as a node to which data is directly input without passing through links in relation to other nodes. Alternatively, the initial input node can be understood as a node that does not have other input nodes connected by links in relation to nodes based on links. The final output node can be understood as a node that does not have an output node in relation to other nodes within the neural network. According to one embodiment, the neural network may include a hidden node. A hidden node can be understood as a node constituting the neural network that is not the initial input node or the final output node.

[0061] According to one embodiment, the number of nodes in the input layer of the neural network may be equal to the number of nodes in the output layer, and the number of nodes may be maintained or increased as progressing from the input layer to the hidden layer. Additionally, the number of nodes may decrease as progressing from the hidden layer to the output layer. In another example, the number of nodes in the input layer of the neural network may be fewer than the number of nodes in the output layer, and the number of nodes may be maintained or increased as progressing from the input layer to the hidden layer. Additionally, the number of nodes may be determined according to the number of classes or results to be predicted as progressing from the hidden layer to the output layer. In yet another example, the number of nodes in the input layer of the neural network may be greater than the number of nodes in the output layer, and the number of nodes may increase as progressing from the input layer to the hidden layer. Additionally, the number of nodes may decrease as progressing from the hidden layer to the output layer.

[0062] Neural networks can be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. The training of a neural network may be the process of applying knowledge to the neural network to perform a specific action.

[0063] Neural network training can be performed in a way that minimizes output errors. To train a neural network, training data is repeatedly input into the network to calculate the network's output and error relative to the data. The gradient calculated from the error can be backpropagated from the output layer to the input layer and used to update the weights of each node. The amount of change in the connection weights of each updated node can be determined by the learning rate. The neural network's calculation of the input data and the backpropagation of the error can constitute a training cycle (epoch). The learning rate can be applied differently depending on the number of iterations in the neural network's training cycle. For example, a high learning rate can be used in the early stages of training to enable the network to quickly achieve performance and increase efficiency, while a low learning rate can be used in the later stages to improve accuracy.

[0064] The parameter definition unit (110) can define a second parameter for each of a plurality of users based on the first parameter, user information, and purchasing tendency (S130).

[0065] According to one embodiment, the second parameter can be determined based on the following mathematical formula.

[0066]

[0067] Is It can be the second parameter for the nth user. Is It can be the first parameter for the nth product. and is a natural number. Is It can indicate the purchase frequency of the nth user. It can indicate a purchase frequency such as once a week, twice a week, or three times a week. Is It can indicate the purchasing propensity of the nth user. It can represent users' purchasing tendencies such as cost-effectiveness, low price, or premium. Is It can indicate the connection status of the platform for the nth user. can indicate a connection status such as an online state or an offline state. The parameter definition unit (110) is For the th user A second parameter such as can be defined. The second parameter is the aforementioned inside In addition, other detailed parameters may be included.

[0068] The grouping unit (130) can determine at least one second user in which a second-2 parameter having a similarity of more than a predetermined threshold with a second-1 parameter of the first user appears (S140). The first user may be a target user. The first user may be a user for providing a user interface that supports gamification.

[0069] According to one embodiment, the grouping unit (130) can determine the similarity between the first purchase tendency and the second purchase tendency based on the following mathematical formula.

[0070]

[0071] It can indicate the similarity between the first purchasing propensity and the second purchasing propensity. may represent the 2-1 parameter related to the 1st user. The 2-1 parameter is It can appear as follows. may represent a second-2 parameter associated with at least one second user. The second-2 parameter is It can appear as follows. can represent the total number of unique parameters included in the 2-1 parameter and the 2-2 parameter.

[0072] For example, the unique parameters included in Parameter 2-1 may be the product category, product price range, user's age, user's gender, user's purchase frequency, user's purchasing propensity, and user's platform connection status. The unique parameters included in Parameter 2-2 may be the product category, product price range, user's age, user's gender, user's purchase frequency, user's purchasing propensity, and user's platform connection status. Since the unique parameters included in Parameter 2-1 and the unique parameters included in Parameter 2-2 are identical, It can be represented by a value such as 7.

[0073] As another example, the unique parameters included in Parameter 2-1 may be the product category, product price range, user's age, user's gender, user's purchase frequency, user's purchasing propensity, user's platform connection status, and user's rank within the platform. The unique parameters included in Parameter 2-2 may be the product category, product price range, user's age, user's gender, user's purchasing propensity, and user's platform connection status. In this case, the unique parameters included in Parameter 2-1 and the unique parameters included in Parameter 2-2 are different, and It can be represented by a value such as 8.

[0074] can represent the number of matching parameters between the 2-1 parameter and the 2-2 parameter. The 2-1 parameter It appears as, and the 2-2 parameter is In cases where it appears as shown, there can be 6 matching parameters. Accordingly, It may appear as a value such as 0.857. The grouping unit (130) can determine at least one second user associated with the second-2 parameter, where the determined similarity appears above a predetermined threshold.

[0075] The grouping unit (130) can determine a first group that groups a first user and at least one second user (S150). The user interface unit (140) can provide a user interface that supports gamification related to the first group (S160). The user interface may include a percentage gauge related to the first user's mission achievement. The percentage gauge can be understood as a graphic object in which a bar, etc., is updated according to the achievement of the mission.

[0076] For example, the percentage gauge may appear as 0%, 70%, 100%, or 120%, depending on the user's completion of a mission. Other percentage gauges may be updated based on user actions. The percentage gauge may be updated based on actions such as the user adding products to a shopping cart or moving to a checkout page.

[0077] The mission determination unit (150) can determine at least one mission to induce a first user to make a purchase (S170). At least one mission can be determined based on a first parameter and a second parameter.

[0078] For example, at least one mission may include a mission to add an unspecified product to a shopping cart, a mission to add a specific product to a shopping cart, a mission to add a greater number of products to a shopping cart than at least one second user in a first group, a mission to add a product purchased by at least one second user in a first group to a shopping cart, or a mission to add a product identical to the product in the shopping cart of at least one second user in a first group to a shopping cart.

[0079] According to one embodiment, the mission determination unit (150) can determine at least one mission through a pre-trained second neural network model. The second neural network model may be a model trained to determine at least one mission using first learning parameters for a plurality of products and second learning parameters for a plurality of learning users. The mission determination unit (150) can determine the purchasing propensity of a plurality of users by inputting the first parameter and the second parameter into the second neural network model.

[0080] The second neural network model may include sLLM (small Large Language Model), SLM (Small Language Model), Large Language Model (LLM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), autoencoder, Generative Adversarial Networks (GAN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Q Network, U Network, Siamese Network, or Generative Adversarial Network (GAN).

[0081] The user interface unit (140) can provide at least one determined mission through the user interface (S180).

[0082] According to an embodiment, the user interface unit (140) may provide a message through the user interface such as “People similar to you have purchased ~~ in the ~~ field. Would you like to purchase? Mission accomplished!” or “You have ~~~ left to reach the TOP 5 monthly purchases in your 30s! When you reach the TOP 5, a ~~~ coupon will be given!” related to at least one mission.

[0083] The user interface unit (140) can update the progress of the percentage gauge according to the achievement of at least one mission (S190).

[0084] The user interface unit (140) can update the progress of the percentage gauge, such as 0%, 70%, 100%, or 120%, depending on the user's achievement of the mission.

[0085] According to one embodiment, the mission determination unit (150) can determine the progress of a percentage gauge related to at least one second user within the first group. The mission determination unit (150) can update a mission related to the first user depending on whether the percentage gauge related to at least one second user is updated. The user interface unit (140) can update a message output through the user interface unit depending on whether the percentage gauge related to at least one second user is updated.

[0086] According to one embodiment, the reward payment unit (160) can determine the reward to be paid to the first user based on the progress of the percentage gauge.

[0087] For example, if the reward payer (160) determines that the percentage gauge has reached 100%, it can determine a pre-set reward mapped to 100%. The reward payer (160) can determine a reward such as free shipping.

[0088] As another example, if the reward payer (160) determines that the percentage gauge has reached 120%, it can determine a pre-set reward mapped to 120%. The reward payer (160) can determine a reward such as a discount benefit.

[0089] As another example, if the reward payment unit (160) determines that the percentage gauge has reached 140%, it can determine a pre-set reward mapped to 140%. The reward payment unit (160) can determine a reward such as providing a certain product for free.

[0090] According to one embodiment, when the reward payment unit (160) decides to pay a reward to the first user, it can determine the reward to be paid to the first user based on the first parameter and the second-1 parameter. The reward payment unit (160) can determine the reward based on the first parameter for each of the plurality of products and the second-1 parameter for the first user. Accordingly, a reward suitable for the first user can be determined.

[0091] For example, the first user may be a user eligible for free shipping based on their tier within the platform. The first user does not need a reward such as free shipping. Therefore, the reward payment unit (160) can determine a reward to be paid to the first user based on the first parameter and the second-1 parameter. For example, the reward payment unit (160) can determine a reward such as a discount benefit based on the first parameter and the second-1 parameter if it is determined that the first user is in a tier eligible for free shipping and that the percentage gauge has reached 100%.

[0092] According to one embodiment, the reward payment unit (160) can determine a reward through a pre-trained third neural network model. The second neural network model may be a model trained to determine at least one mission using first training parameters for a plurality of products and second training parameters for a plurality of training users. The reward payment unit (160) can determine a reward for a plurality of users by inputting the first parameter and the second parameter into the third neural network model.

[0093] The third neural network model may include sLLM (small Large Language Model), SLM (Small Language Model), Large Language Model (LLM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), autoencoder, Generative Adversarial Networks (GAN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Q Network, U Network, Siamese Network, or Generative Adversarial Network (GAN).

[0094] The control unit (190) can reflect a reward in the payment according to the user interaction of the first user who purchases a product related to the achievement of at least one mission.

[0095] According to the configuration described above, the computing device (100) can enhance user interest by supporting gamification based on consumption similarity. Accordingly, the user's purchase conversion rate can be improved. Also, since rewards are provided at the payment stage, the rate at which users leave products in their shopping cart can be reduced. In other words, the probability of users completing a payment can be increased. Because it provides a gamified reward experience that goes beyond simple discounts, users can purchase products without losing interest.

[0096] FIG. 3 is a drawing for illustrating an example of a user interface provided through a user interface unit according to some embodiments of the present disclosure.

[0097] Referring to FIG. 3, the user interface (200) may include a percentage gauge (210) and a mission (220).

[0098] The user interface section (140) can update the progress of the percentage gauge (210) according to the achievement of the mission (220).

[0099] FIG. 4 is a drawing for illustrating another example of a user interface provided through a user interface section according to some embodiments of the present disclosure.

[0100] Referring to FIG. 4, the user interface (300) may include a percentage gauge (310) and a mission (320).

[0101] The user interface section (140) can update the progress of the percentage gauge (310) according to the achievement of the mission (320).

[0102] Description of the presented embodiments is provided so that a person skilled in the art may use or practice the present disclosure. Various modifications to these embodiments will be apparent to a person skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments presented herein, but should be interpreted in the broadest possible scope consistent with the principles and novel features presented herein.

Claims

Claim 1 A method for supporting gamification based on consumption similarity performed by a computing device comprising at least one processor, comprising: defining a first parameter for each of a plurality of products based on product information for a plurality of products and purchase records of a plurality of users associated with said plurality of products; determining a purchase propensity of a plurality of users based on an interaction from a plurality of users selecting at least one product among said plurality of products and user information associated with said plurality of users; defining a second parameter for each of said plurality of users based on the first parameter, said user information and said purchase propensity; determining at least one second user in which a second-2 parameter having a similarity of at least a predetermined threshold with a second-1 parameter of a first user appears; determining a first group grouping the first user and the at least one second user; providing a user interface supporting gamification associated with said first group - said user interface includes a percentage gauge associated with the achievement of a mission by said first user -; determining at least one mission to induce a purchase by said first user - said at least one mission is determined based on said first parameter and said second parameter A method for supporting gamification based on consumption similarity, comprising: a step of providing at least one determined mission through the user interface; and a step of updating the progress of the percentage gauge according to the achievement of the at least one mission. Claim 2 In claim 1, the first parameter is a mathematical formula Determined based on, and the above silver Indicates the category of the nth product, and the above The above Indicates the price range of the th product and the above The above Indicates the main purchasing age group of the th product, and the above The above A method to support gamification based on consumption similarity, indicating the main purchasing gender of the th product. Claim 3 In claim 1, the similarity is a mathematical formula Determined based on, and the above represents the 2-1 parameter related to the above-mentioned first user, and the above represents a second-2 parameter related to at least one second user, and the represents the total number of unique parameters included in the above 2-1 parameter and the above 2-2 parameter, and the A method for supporting gamification based on consumption similarity, which indicates the number of matching parameters between the above-mentioned 2-1 parameter and the above-mentioned 2-2 parameter. Claim 4 A method for supporting gamification based on consumption similarity, further comprising: a step of determining a reward to be paid to the first user based on the progress of the percentage gauge in claim 1; and a step of reflecting the reward in payment according to the user interaction of the first user who purchases a product related to the achievement of at least one mission. Claim 5 A computing device that supports gamification based on consumption similarity, comprising: a parameter definition unit that defines a first parameter for each of a plurality of products based on product information for a plurality of products and purchase records of a plurality of users related to the plurality of products, and defines a second parameter for each of the plurality of users based on the first parameter, user information related to the plurality of users, and the purchasing propensity of the plurality of users; a purchasing propensity determination unit that determines the purchasing propensity of the plurality of users based on an interaction from the plurality of users selecting at least one product among the plurality of products and user information related to the plurality of users; a grouping unit that determines at least one second user in which a second-2 parameter having a similarity of at least a predetermined threshold with a second-1 parameter of the first user appears, and determines a first group formed by grouping the first user and the at least one second user; and a user that provides a user interface that supports gamification related to the first group, - the user interface includes a percentage gauge related to the achievement of a mission by the first user - and provides at least one mission through the user interface, and updates the progress of the percentage gauge according to the achievement of the at least one mission. A computing device that supports gamification based on consumption similarity, comprising: an interface unit; and a mission determination unit that determines at least one mission to induce a purchase by the first user, wherein the at least one mission is determined based on the first parameter and the second parameter.

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