KR102653642B1 - Method, computer program, and server for recommending goods - Google Patents

Abstract

A method for product recommendation performed by a server is disclosed. The method includes calculating a product scoring value of a first product group for a plurality of predetermined indices; determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate groups; and determining a final recommended product from the determined second product group using a product recommendation algorithm. may include.

Description

Method, computer program, and server for product recommendation {METHOD, COMPUTER PROGRAM, AND SERVER FOR RECOMMENDING GOODS}

The present invention relates to a method, computer program, and server for product recommendation, and particularly to a method, computer program, and server for personalized product recommendation.

As the number of Internet shopping malls increases with the development of the Internet, the number of products that customers can purchase through shopping malls is also increasing in variety. When choosing a product, customers tend to refer to evaluations or recommendations of products that others have experienced or owned. For example, when a product is searched on a search service, evaluations or recommendations for various products are presented as search results, and the user can select a product suitable for him/her based on this information. Accordingly, customers spend a considerable amount of time searching for desired products. In addition, the reliability of product evaluations and recommendations is decreasing due to the proliferation of advertising information taking advantage of this situation. Moreover, since each person has a different personality, their tastes in preferred products are also different, so if you choose a product based on other people's product evaluations or recommendations, you will actually end up choosing a product that does not suit your tastes. There is a problem. For this reason, many recommendation systems that can recommend products suitable for customers are being developed.

A recommendation system is a type of personalization technology and may refer to recommending content or information that the user may be interested in to the user. Among various recommendation technologies, a recommendation method based on a collaborative filtering algorithm is widely used. The recommendation method using a collaborative filtering algorithm is a personalization technique that recommends related products to a customer by using the preferences of other customers similar to the customer's preferred pattern.

However, this recommendation method based on a collaborative filtering algorithm requires a large amount of customer behavioral information from existing users to improve data accuracy, so it costs a lot of money to collect and analyze data. Additionally, if the amount of data is enormous, the time required to calculate similarity increases exponentially, which may cause problems with service scalability.

Additionally, when using a collaborative recommendation algorithm, there is a cold start problem that makes it difficult to respond to new customers, new products, and new situations that have not been accumulated in advance. Various methods are being attempted to address the shortcomings of these collaborative filtering algorithms.

The present disclosure was developed in response to the above-described background technology, and provides a method for product recommendation.

In order to solve the above-mentioned problems, a method for product recommendation performed by a server is disclosed. The method for product recommendation includes: calculating a product scoring value of a first product group for a plurality of predetermined indices; determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate groups; and determining a final recommended product from the determined second product group using a product recommendation algorithm. may include.

Additionally, the plurality of predetermined indices may include at least one of a digital index, a living index, a well-being index, a surrounding environment index, and a health index.

Additionally, the product recommendation algorithm may include a collaborative filtering algorithm.

Additionally, the factors used in the collaborative filtering algorithm may include user-like information including at least one of gender, age, family, occupation, residential area, level of interest, purchase level, and owned products.

Additionally, factors used in the collaborative filtering algorithm may include post-management information.

Additionally, the post-management information may include first post-management sub-information related to the user and second post-management sub-information related to the product.

Additionally, the first post-management sub-information has a higher user-related weight compared to the second post-management sub-information, and the second post-management sub-information has a higher product-related weight than the first post-management sub-information. You can.

Additionally, the product recommendation algorithm may include a plurality of algorithms, and the plurality of algorithms may include at least a collaborative filtering algorithm and a machine learning model.

Additionally, the machine learning model may be at least partially learned by a training set generated based on the collaborative filtering algorithm.

A computer program stored in a computer-readable storage medium for solving the above-mentioned problems is disclosed. The computer program includes instructions for causing one or more processors to perform a method for performing object-centric data augmentation, the instructions comprising: calculating a product scoring value of a first product group for a plurality of predetermined indices; determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate groups; and determining a final recommended product from the determined second product group using a product recommendation algorithm. may include.

A server that performs a method for product recommendation to solve the above-described problem is disclosed. The server has: memory; and processor; , wherein the processor: calculates a product scoring value of the first product group for a plurality of predetermined indices; determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate groups; Then, a final recommended product can be determined from the determined second product group using a product recommendation algorithm.

The present disclosure may provide a method for product recommendation.

1 is a diagram illustrating an example of a system for product recommendation according to some embodiments of the present disclosure.
Figure 2 is a diagram showing an example of the configuration of a management server according to some embodiments of the present disclosure.
Figure 3 is a flowchart for explaining a method for product recommendation according to an embodiment of the present disclosure.
Figure 4 is a schematic diagram showing a network function for a machine learning model according to an embodiment of the present disclosure.
Figure 5 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the disclosure. However, it is clear that these embodiments may be practiced without these specific descriptions.

As used herein, the terms “component,” “module,” “system,” and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or an implementation of software. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. Additionally, these components can execute from various computer-readable media having various data structures stored thereon. Components can transmit signals, for example, with one or more data packets (e.g., data and/or signals from one component interacting with other components in a local system, a distributed system, to other systems and over a network such as the Internet). Depending on the data being transmitted, they may communicate through local and/or remote processes.

Additionally, the term “or” is intended to mean an inclusive “or” and not an exclusive “or.” That is, unless otherwise specified or clear from context, “X utilizes A or B” is intended to mean one of the natural implicit substitutions. That is, either X uses A; X uses B; Or, if X uses both A and B, “X uses A or B” can apply to either of these cases. Additionally, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the related listed items.

Additionally, the terms “comprise” and/or “comprising” should be understood to mean that the corresponding feature and/or element is present. However, the terms “comprise” and/or “comprising” should be understood as not excluding the presence or addition of one or more other features, elements and/or groups thereof. Additionally, unless otherwise specified or the context is clear to indicate a singular form, the singular terms herein and in the claims should generally be construed to mean “one or more.”

And, the term “at least one of A or B” should be interpreted to mean “a case containing only A,” “a case containing only B,” and “a case of combining A and B.”

Those skilled in the art will additionally recognize that the various illustrative logical blocks, components, modules, circuits, means, logic, and algorithm steps described in connection with the embodiments disclosed herein may be implemented using electronic hardware, computer software, or a combination of both. It must be recognized that it can be implemented with To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or software will depend on the specific application and design constraints imposed on the overall system. A skilled technician can implement the described functionality in a variety of ways for each specific application. However, such implementation decisions should not be construed as causing a departure from the scope of the present disclosure.

The description of the presented embodiments is provided to enable anyone skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art. The general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Therefore, the present invention is not limited to the embodiments presented herein. The present invention is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

In this disclosure, network function, artificial neural network, and neural network may be used interchangeably.

1 is a diagram illustrating an example of a system for product recommendation according to some embodiments of the present disclosure.

As shown in FIG. 1, the system for product recommendation may include a management server 100, client terminals 200a, 200b, and 200c: 200, and a communication network 300. The components shown in FIG. 1 are exemplary, and additional components may exist or some of the components shown in FIG. 1 may be omitted.

Although not shown in FIG. 1, the client terminal 200 and management server 100 may include a memory, a communication unit, and a processor.

The client terminal 200 has a mechanism for communicating with the management server 100 through the communication network 300, and may refer to any type of node in a system for product recommendation. For example, the client terminal 200 may include a PC, laptop computer, workstation, terminal, and/or any electronic device having network connectivity. Additionally, the client terminal 200 may include an arbitrary server implemented by at least one of an agent, an application programming interface (API), and a plug-in. Additionally, the client terminal 200 may include an application source and/or a client application.

The client terminal 200 may be any entity capable of processing and storing arbitrary data, including a processor and a storage unit (memory and persistent storage media).

The client terminal 200 may connect to the management server 100 and transmit information necessary for product recommendation to the management server 100 during the process of purchasing a product or performing post-management of the product. Additionally, the client terminal 200 may receive information and responses processed by the management server 100 with respect to the transmitted information.

Management server 100 according to some embodiments of the present disclosure may include any type of computer system or computer device, such as, for example, a microprocessor, mainframe computer, or digital processor. Although not shown in FIG. 1, the management server 100 may include a memory, a communication unit, and a processor.

Communication networks 300 according to some embodiments of the present disclosure include Public Switched Telephone Network (PSTN), x Digital Subscriber Line (xDSL), Rate Adaptive DSL (RADSL), Multi Rate DSL (MDSL), and VDSL. Various wired communication systems such as (Very High Speed DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and local area network (LAN) can be used.

In addition, the communication network 300 presented in the present disclosure includes Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multi Access (OFDMA), and SC-FDMA. Various wireless communication systems can be used, such as (Single Carrier-FDMA) and other systems. The techniques described in this disclosure may be used in the networks mentioned above, as well as in any type of other communication network.

Figure 2 is a diagram showing an example of the configuration of a management server according to some embodiments of the present disclosure.

As shown in FIG. 2, the management server 100 may include a processor 110, a memory 120, and a communication unit (not shown).

The processor 110 may be composed of one or more cores, and may include a central processing unit (CPU), a general purpose graphics processing unit (GPGPU), and a tensor processing unit (TPU) of a computing device. It may include any type of processor for product recommendation by executing instructions stored in memory, such as unit). The processor may read the computer program stored in the memory and process methods for product recommendation according to some embodiments of the present disclosure. The processor may control the overall operations of the components of the management server 100 to process a method for product recommendation.

The processor 110 of the management server 100 may control the overall operations of the components of the management server 100 in order to process a method for product recommendation.

The management server 100 may further include memory 120. The memory may store programs for operating the processor 110, and may temporarily or permanently store input/output data. Memory is a flash memory type, hard disk type, multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), and RAM (Random Access). Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, optical disk It may include at least one type of storage medium. These memories can be operated under processor control. Additionally, memory and storage may be used interchangeably with each other in the present disclosure.

The communication unit (not shown) may include a wired/wireless Internet module for network connection. The communication unit may perform communication with the client terminal 200. Wireless Internet technologies include Wireless LAN (WLAN) (Wi-Fi), Wireless Broadband (Wibro), World Interoperability for Microwave Access (Wimax), and High Speed Downlink Packet Access (HSDPA). Wired Internet technologies include Digital Subscriber Line (XDSL), Fibers to the home (FTTH), and Power Line Communication (PLC).

The above-described components are exemplary and the scope of the present disclosure is not limited to the above-described components. That is, additional components may be included or some of the above-described components may be omitted depending on the implementation aspect of the embodiments of the present disclosure.

Hereinafter, a method for product recommendation according to some embodiments of the present disclosure will be described.

Figure 3 is a flowchart for explaining a method for product recommendation according to an embodiment of the present disclosure.

The method for product recommendation of the present disclosure can generate a final recommended product using two-step classification. Specifically, the method for product recommendation of the present disclosure performs primary classification for a plurality of products using a plurality of indices, and then performs secondary classification using a product recommendation algorithm to determine the final recommended product. The method for product recommendation of the present disclosure increases the accuracy of recommended products by reducing the objects classified by the product recommendation algorithm that consumes a lot of computer resources and computational time through primary classification, and is advantageous in terms of computer resources and computational time consumed. It can have points.

According to some embodiments of the present disclosure, a method for product recommendation may include steps s100 to s130 described below. However, these steps are described for illustrative purposes only, and some steps may be changed or omitted, or additional steps may be added. Additionally, these steps may be performed in any order and are not necessarily performed in the order described below.

According to some embodiments of the present disclosure, a method for product recommendation may include calculating a product scoring value of a first product group for a plurality of predetermined indices (s100).

The plurality of predetermined indices may include various indices representing the characteristics of the product. A plurality of predetermined indices may be used to determine a second product group corresponding to the first product group among product candidates.

The first product group may include one or more products purchased or owned by the user. For example, the first product group may be determined by information indicating products purchased or owned by the user. As another example, the first product group may be determined by production/sales/distribution information of the producer, seller, distributor, etc. of the product purchased or owned by the user. As another example, the first product group may include one or more products selected by the user as products of interest. However, it is not limited to this.

According to some embodiments of the present disclosure, the plurality of predetermined indices may include at least one of a digital index, a living index, a well-being index, a surrounding environment index, and a health index. However, these indices are explained for illustrative purposes only, and some indices may be changed or omitted, or additional indices may be added.

The digital index may include a value that quantifies the degree of technological advancement of the product. For example, the digital index can be determined by considering the product launch date, technology and parts used, price ranking within the same product group, digital characteristic values of the product group, etc.

The living index may include a value that quantifies the degree to which the product is essential for daily life. For example, the living index may be determined by considering the number of holders (or households owning the product), possession ratio, and living characteristics of the product group, etc.

The well-being index may include a value that quantifies the degree to which the product contributes to the convenience of life. For example, the well-being index may be determined by considering the life satisfaction and convenience of the owner of the product, the well-being characteristic value of the product group, etc.

The surrounding environment index may include a value that quantifies the degree of relationship between the product and cleanliness/environment. For example, the environmental index may be determined by considering the material of the product, manufacturing process, product lifespan, disposal method, and environmental characteristics of the product group.

The health index may include a value that quantifies the degree of relationship between the product and health. For example, the health index may be determined by considering the health-related functions of the product, the characteristic values of the product group, etc.

According to some embodiments of the present disclosure, a product scoring value may be determined by calculating a plurality of predetermined indices for the first product group. For example, the digital index of the first product group may be 1.5, the living index may be 1.2, the well-being index may be 0.7, the surrounding environment index may be 2.0, and the health index may be 0.8. In this case, the product scoring value of the first product group may be 6.2. However, it is not limited to this.

When the first product group includes a plurality of products, the product scoring value may be determined using a plurality of predetermined indices for each of the plurality of products included in the first product group. For example, when the first product group includes a plurality of products, the product scoring value of the first product group may be the average of a plurality of predetermined indices calculated for each of the plurality of products included in the first product group. However, it is not limited to this, and when the first product group includes a plurality of products, the product scoring value can be determined in various ways.

According to some embodiments of the present disclosure, the product scoring value may be determined by calculating weights for each of a plurality of predetermined indices for the first product group. For example, the weight may be determined by reflecting user characteristic information. User characteristic information may be determined by the user's gender, age, family, occupation, region, interest, purchasing power, user information holding the same product, etc. For example, if the user characteristic information includes information indicating that the user's family is a family of four or more people, high weight may be given to the living index and well-being index. As another example, if the user characteristic information includes information indicating that the user's main interest is well-being and health, high weight may be given to the well-being index and health index.

As another example, the weight may be determined by reflecting product characteristic information of the first product group. For example, product characteristic information may be determined by the type and function of the product, the age group/gender/residence of the product's main consumers, the consumption pattern of the product, the price of the product, etc. For example, if product characteristic information includes information indicating high importance to the digital index, a high weight may be assigned to the digital index. As another example, if product characteristic information includes information indicating high importance to the living index and well-being index, high weight may be assigned to the living index and well-being index. However, the weight is not limited to this and may be determined in various ways.

According to some embodiments of the present disclosure, a method for product recommendation may include determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate group (s110). .

The product candidate group may include products of the same or similar type as the first product group. For example, if the first product group is a smartphone, the product candidate group may include smartphones, smartwatches, tablets, etc. Additionally, the second product group may include products that are highly connected to the first product group. For example, if the first product group is a washing machine, the product candidate group may be a dryer. However, it is not limited to this, and product candidates can be determined in various ways.

Product candidate groups can be determined in various ways depending on the purpose of product recommendation. For example, when the management server 100 determines to perform a method for product recommendation in response to a user's product search command, the management server 100 selects products of the same or similar type as the first product group into the product candidate group. can be decided. As another example, when the management server 100 determines to perform a method for product recommendation for the purpose of advertising to a user, the management server 100 may determine a product highly correlated with the first product group as a product candidate group. However, it is not limited to this, and product candidates may be determined according to various purposes.

Whether the product corresponds to the product scoring value can be determined by comparing the product scoring value of each product (or product group consisting of a plurality of products) included in the product candidate group with the product scoring value of the first product group. For example, a predetermined range for the product scoring value of the first product group among the product candidate groups (e.g., when the product scoring values have values between 0 and 100, a value of +5 to -5 of the determined product scoring value) A product having a product scoring value included within may be determined as the second product group. As another example, among the product candidate groups, n top products (e.g., 2 top products, 3 top products, etc.) having values close to the product scoring value of the first product group may be determined as the second product group. However, it is not limited to this, and the second product group can be determined in various ways depending on the product scoring value.

According to some embodiments of the present disclosure, a method for product recommendation may include determining a final recommended product from the determined second product group using a product recommendation algorithm (s120).

In step s120, the final recommended product may be determined through a product recommendation algorithm among the first classified products (i.e., the second product group) using the product scoring value determined by a plurality of predetermined indices. The product recommendation algorithm corresponding to the secondary classification can be implemented in the following way.

According to some embodiments of the present disclosure, the product recommendation algorithm may include a collaborative filtering algorithm. A collaborative filtering algorithm may be a technique for classifying users who exhibit similar patterns in preferences based on information about the user and the user's preferences. For example, the collaborative filtering algorithm may include user-based collaborative filtering that determines recommended products by filtering out items consumed or highly rated by at least one other user with similar consumption tendencies as the user. Additionally, the collaborative filtering algorithm may include item-based collaborative filtering that determines recommended products by filtering items that are consumed similarly or evaluated similarly to a combination of a predetermined number of items consumed by the user.

For example, when predicting a specific user's preference for a specific item, a user-based collaborative filtering algorithm may be an algorithm that performs prediction based on the preference for a specific item of a user similar to the specific user with respect to other items. . Specifically, when predicting the evaluation score r _u,i for item i of user u, k similar users v with the highest similarity of evaluation scores to other items are extracted and similar user evaluation scores r _v for i. _{, i} can be used to predict the evaluation score r _{u, i} through the following equation.

In the above equation, N(u;i) is a set of users similar to a specific user u, and s(u,v) represents the similarity between user u and another user v. However, it is not limited to this, and the collaborative filtering algorithm can be implemented in various ways.

According to some embodiments of the present disclosure, factors used in the collaborative filtering algorithm may include user-like information including at least one of gender, age, family, occupation, residential area, interest, purchase level, and owned products.

Factors of the collaborative filtering algorithm may include information for determining the degree of similarity between a user who receives a recommended product and a plurality of other users. For example, user-like information may include, but is not limited to, gender, age, family, occupation, residential area, interests, purchase level, and products owned.

According to some embodiments of the present disclosure, the factors used in the collaborative filtering algorithm include at least one of the type of product, function, age/gender/residence of the main consumer group of the product, consumption type of the product, and price of the product. May contain similar information. If the argument of the collaborative filtering algorithm includes product similarity information, the collaborative filtering algorithm may be an item-based hybrid collaborative filtering algorithm that uses similarity information between items (i.e., products).

Similarity between users can be determined through user similarity information including the above-described factors. Additionally, a group of users similar to the user may be determined based on the determined degree of similarity between users. In this case, the final recommended product can be determined by determining the evaluation score for each product included in the second product group through a collaborative filtering algorithm implemented according to Equation 1.

According to some embodiments of the present disclosure, factors used in a collaborative filtering algorithm may include post-management information. Post-management information may refer to information for product management to resolve product failures, inconveniences, etc. after purchasing the product. Post-management information may include various information that can be used to determine whether users are similar and whether products are similar. Examples of follow-up information include:

According to some embodiments of the present disclosure, the after-management information may include first after-management sub-information related to the user and second after-management sub-information related to the product.

The first post-management sub-information may include information about the user among post-management information. For example, the first post-management sub-information may include the user's post-management history information. Specifically, for example, the first follow-up management sub-information includes information on the number of times a user has applied for after-care, after-care application date information, after-care application content information, user after-care cost information, remaining period of free after-care, and after-care evaluation information. It can be included. The first post-management sub-information can be used to determine whether users are similar. However, it is not limited to this, and the first post-management sub-information may include various information.

The second post-management sub-information may include information about the product among the post-management information. For example, the second post-management sub-information may include post-management information determined by the product regardless of the user. Specifically, for example, the second after-sales service sub-information includes after-sales service entity information (e.g., whether the producer or seller is responsible), after-sales service policy evaluation information, information on free-of-charge period and free repair conditions, and after-sales service regarding parts. Quality assurance information including information, after-sales service center information (number, location, and distribution of after-sales service centers, etc.), product after-sales service cost information regarding after-sales service cost addition system and guidance, after-sales service-related transportation information, on-site repair information , it may include the follow-up management period for the period during which follow-up management is possible, product life cycle information according to follow-up management contents and timing, etc. The second post-management sub-information can be used to determine whether the product is similar. In this case, the collaborative filtering algorithm may include an item-based hybrid collaborative filtering algorithm that uses similarity information between items (i.e., products). The collaborative filtering algorithm is not limited to this, and the second post-management sub-information may include various information.

According to some embodiments of the present disclosure, the first after-management sub-information has a higher user-related weight compared to the second after-management sub-information, and the second after-management sub-information has a higher product-related weight compared to the second after-management sub-information. Can have weights.

As described above, the first post-management sub-information can be used to determine whether users are similar. However, the first post-management sub-information can be used to determine product similarity information. Additionally, the second post-management sub-information can be used to determine whether the products are similar. However, the second post-management sub-information can be used to determine whether users are similar. In this case, the first post-management sub-information may have a higher user-related weight compared to the second post-management sub-information. Therefore, the first post-management sub-information can be reflected at a higher rate in determining user similarity than the second post-management sub-information. Additionally, the second follow-up management sub-information may have a higher product-related weight than the first follow-up management sub-information. Therefore, the second follow-up management sub-information can be reflected at a higher rate in determining product similarity compared to the first follow-up management sub-information. However, it is not limited to this, and the first post-management sub-information and the second post-management sub-information may have various ranges of user-related weights and product-related weights.

In some embodiments according to the present disclosure, post-management information may be used as an additional factor for the user and product in the method for product recommendation, thereby increasing the accuracy of product recommendation. Additionally, when it is difficult to ensure the accuracy of the collaborative filtering algorithm due to a lack of data about users and products, it can be used as information about other similar users and similar products according to the user-related weight and product-related weight.

According to some embodiments of the present disclosure, the product recommendation algorithm includes a plurality of algorithms, and the plurality of algorithms may include at least a collaborative filtering algorithm and a machine learning model.

The product recommendation algorithm may include multiple algorithms. For example, the product recommendation algorithm may include the collaborative filtering algorithm described above. Additionally, the product recommendation algorithm may include a machine learning model along with the collaborative filtering algorithm described above.

A machine learning model can be one of the artificial intelligence algorithms. Artificial intelligence refers to the field of researching artificial intelligence or methodologies to create it, and machine learning refers to the field of defining various problems dealt with in the field of artificial intelligence and researching methodologies to solve them. can do. Machine learning can be defined as an algorithm that improves the performance of a certain task through consistent experience.

Artificial Neural Network (ANN) is a model used in machine learning and can refer to an overall model with problem-solving capabilities that is composed of artificial neurons (nodes) that form a network through the combination of synapses. Artificial neural networks can be defined by connection patterns between neurons in different layers, a learning process that updates model parameters, and an activation function that generates output values.

An artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include synapses connecting neurons. In an artificial neural network, each neuron can output the activation function value for the input signals, weight, and bias input through the synapse.

Model parameters may refer to parameters determined through learning. Model parameters may include weights of synaptic connections and biases of neurons. And, hyperparameters may refer to parameters that must be set before learning in a machine learning algorithm. Hyperparameters may include learning rate, number of iterations, mini-batch size, initialization function, etc.

The purpose of learning a machine learning model can be seen as determining model parameters that minimize the loss function. The loss function can be used as an indicator to determine optimal model parameters during the learning process of a machine learning model.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning depending on the learning method.

Supervised learning may refer to a method of training an artificial neural network given a label for learning data. Here, the label may mean the correct answer (or result value) that the artificial neural network must infer when learning data is input to the artificial neural network. Unsupervised learning can refer to a method of training an artificial neural network in a state where no labels for training data are given. Reinforcement learning can refer to a learning method in which an agent defined within an environment learns to select an action or action sequence that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented with a deep neural network (DNN: Deep Neural Network) that includes multiple hidden layers is also called deep learning, and deep learning is a part of machine learning. Hereinafter, machine learning is used to include deep learning.

According to some embodiments of the present disclosure, the final recommended product may be determined through a machine learning model using at least one of user similarity information, product similarity information, and follow-up management information as a factor. For example, a recommendation probability may be obtained for each product included in the second product group by using at least one of user similarity information, product similarity information, or follow-up management information as a factor. In this case, the product with the highest recommendation probability may be determined as the recommended product. As another example, the top n products (e.g., n = 2, 3, etc.) in order of high recommendation probability may be determined as recommended products.

Product recommendation algorithms, including collaborative filtering algorithms and machine learning models, can show higher accuracy as the amount of data increases. Therefore, the data used in the product recommendation algorithm can be increased using the following method.

According to some embodiments of the present disclosure, a machine learning model may be learned at least in part by a training set generated based on the collaborative filtering algorithm. For example, factors used in the collaborative filtering algorithm, final product recommendation information generated in response to the used factors, product information selected by the actual user, etc. may be stored on the server. In this case, the stored data can be used, at least in part, to create a training set for training a machine learning model. For example, a training set can be created by matching the factors used in the collaborative filtering algorithm with product information selected by the actual user. In this case, a high-quality dataset can be created because the factors used in collaborative filtering are matched with product information selected by actual users.

As another example, a training set may be created by matching the factors used in the collaborative filtering algorithm with the final recommended product information generated in response to the used factors. Since it is often difficult to obtain product information selected by actual users for final product recommendation information, this method can be useful for generating a large amount of data sets while ensuring high accuracy through a collaborative filtering algorithm. However, it is not limited to this, and the data generated by the collaborative filtering algorithm can be used to create a training set in various ways.

The generated training set can be used to generate additional training sets by adjusting the values of the parameters used. For example, an additional training set can be created by adding values within an allowable error range to the values of factors included in the generated data set. The allowable error range may have values such as 1%, 5%, etc., but is not limited thereto.

According to some embodiments of the present disclosure, when a method for product recommendation uses a plurality of product recommendation algorithms, the factors used in the plurality of product recommendation algorithms are preferably the same, but may be different from each other. For example, a collaborative filtering algorithm may use user-like information as a factor, and a machine learning model may use product-like information and follow-up management information as factors. As another example, a collaborative filtering algorithm may use user-like information as a factor, and a machine learning model may use user-like information and product-like information as factors. As another example, the collaborative filtering algorithm may use only some of the factors used in machine learning models such as user-like information, product-like information, and follow-up management information. When different factors are used between a plurality of algorithms, it can have the effect of providing recommended information to users from various perspectives. In addition, when the final recommended product information generated by different algorithms is different, the user is provided with the opportunity to select a product from the final recommended products from various perspectives by providing the user with information about the factors used in each of the plurality of different algorithms. can be provided. However, it is not limited to this, and recommended products may be determined through collaborative filtering algorithms and machine learning models using various factors.

When different recommended products are obtained using a plurality of product recommendation algorithms, the final recommended product may be determined according to the respective priorities or weights of the plurality of product recommendation algorithms. Priority may be determined by the user's preference for multiple product recommendation algorithms, evaluation of recommendation results, etc. The weight of each of the plurality of product recommendation algorithms may be set by evaluating the recommendation results. The weight can be used as a weight for the probability of a recommended product. In this case, the recommended product that obtains the highest result value among the plurality of recommended products may be determined as the final recommended product. However, it is not limited to this, and the final recommended product may be determined from among different recommended products determined by a plurality of product recommendation algorithms in various ways.

As described above, the method for product recommendation of the present disclosure can generate a final recommended product using two-step classification. The method for product recommendation of the present disclosure increases the accuracy of recommended products by reducing the number of objects classified by a product recommendation algorithm that consumes a lot of computer resources and computational time through primary classification using a plurality of indices, while increasing the computer resources consumed. This can have an advantage in computation time. In addition, by identifying the user's tendency after purchasing a product and using a follow-up management factor that provides additional information about the product, higher accuracy can be guaranteed compared to existing product recommendation methods.

Figure 4 is a schematic diagram showing a network function for a machine learning model according to an embodiment of the present disclosure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node. Nodes (or neurons) that make up neural networks may be interconnected by one or more links.

Within a neural network, one or more nodes connected through a link may form a relative input node and output node relationship. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the data of the output node may be determined based on the data input to the input node. Here, the link connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the neural network. The characteristics of the neural network can be determined according to the number of nodes and links within the neural network, the correlation between the nodes and links, and the value of the weight assigned to each link. For example, if the same number of nodes and links exist and two neural networks with different weight values of the links exist, the two neural networks may be recognized as different from each other.

A neural network may consist of a set of one or more nodes. A subset of nodes that make up a neural network can form a layer. Some of the nodes constituting the neural network may form one layer based on the distances from the first input node. For example, a set of nodes with a distance n from the initial input node may constitute n layers. The distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node. However, this definition of a layer is arbitrary for explanation purposes, and the order of a layer within a neural network may be defined in a different way than described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes in the neural network through which data is directly input without going through links in relationships with other nodes. Alternatively, in a neural network network, in the relationship between nodes based on links, it may mean nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes that do not have an output node in their relationship with other nodes among the nodes in the neural network. Additionally, hidden nodes may refer to nodes constituting a neural network other than the first input node and the last output node.

The neural network according to an embodiment of the present disclosure is a neural network in which the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as it progresses from the input layer to the hidden layer. You can. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be less than the number of nodes in the output layer, and the number of nodes decreases as it progresses from the input layer to the hidden layer. there is. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes in the input layer may be greater than the number of nodes in the output layer, and the number of nodes increases as it progresses from the input layer to the hidden layer. You can. A neural network according to another embodiment of the present disclosure may be a neural network that is a combination of the above-described neural networks.

A deep neural network (DNN) may refer to a neural network that includes multiple hidden layers in addition to the input layer and output layer. Deep neural networks allow you to identify latent structures in data. In other words, it is possible to identify the potential structure of a photo, text, video, voice, or music (e.g., what object is in the photo, what the content and emotion of the text are, what the content and emotion of the voice are, etc.) . Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBM). machine), deep belief network (DBN), Q network, U network, Siamese network, Generative Adversarial Network (GAN), etc. The description of the deep neural network described above is only an example and the present disclosure is not limited thereto.

In one embodiment of the present disclosure, the network function may include an autoencoder. An autoencoder may be a type of artificial neural network to output output data similar to input data. The autoencoder may include at least one hidden layer, and an odd number of hidden layers may be placed between input and output layers. The number of nodes in each layer may be reduced from the number of nodes in the input layer to an intermediate layer called the bottleneck layer (encoding), and then expanded symmetrically and reduced from the bottleneck layer to the output layer (symmetrical to the input layer). Autoencoders can perform nonlinear dimensionality reduction. The number of input layers and output layers can be corresponded to the dimension after preprocessing of the input data. In an auto-encoder structure, the number of nodes in the hidden layer included in the encoder may have a structure that decreases as the distance from the input layer increases. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and decoder) is too small, not enough information may be conveyed, so if it is higher than a certain number (e.g., more than half of the input layers, etc.) ) may be maintained.

A neural network may be trained in at least one of supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. Learning of a neural network may be a process of applying knowledge for the neural network to perform a specific operation to the neural network.

Neural networks can be trained to minimize output errors. In neural network learning, learning data is repeatedly input into the neural network, the output of the neural network and the error of the target for the learning data are calculated, and the error of the neural network is transferred from the output layer of the neural network to the input layer in the direction of reducing the error. This is the process of updating the weight of each node in the neural network through backpropagation. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (i.e., labeled learning data), and in the case of non-teacher learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of teacher learning regarding data classification, the learning data may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and the error can be calculated by comparing the output (category) of the neural network with the label of the training data. As another example, in the case of non-teachable learning for data classification, the error can be calculated by comparing the input training data with the neural network output. The calculated error is backpropagated in the reverse direction (i.e., from the output layer to the input layer) in the neural network, and the connection weight of each node in each layer of the neural network can be updated according to backpropagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The neural network's calculation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently depending on the number of repetitions of the learning cycle of the neural network. For example, in the early stages of neural network training, a high learning rate can be used to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and in the later stages of training, a low learning rate can be used to increase accuracy.

In the learning of neural networks, the training data can generally be a subset of real data (i.e., the data to be processed using the learned neural network), and thus the error for the training data is reduced, but the error for the real data is reduced. There may be an incremental learning cycle. Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which a neural network that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, dropout to disable some of the network nodes during the learning process, and use of a batch normalization layer can be applied. You can.

According to an embodiment of the present disclosure, a computer-readable medium storing a data structure is disclosed.

Data structure can refer to the organization, management, and storage of data to enable efficient access and modification of data. Data structure can refer to the organization of data to solve a specific problem (e.g., retrieving data, storing data, or modifying data in the shortest possible time). A data structure may be defined as a physical or logical relationship between data elements designed to support a specific data processing function. Logical relationships between data elements may include connection relationships between user-defined data elements. Physical relationships between data elements may include actual relationships between data elements that are physically stored in a computer-readable storage medium (e.g., a persistent storage device). A data structure may specifically include a set of data, relationships between data, and functions or instructions applicable to the data. Effectively designed data structures allow computing devices to perform computations while minimizing the use of the computing device's resources. Specifically, computing devices can increase the efficiency of operations, reading, insertion, deletion, comparison, exchange, and search through effectively designed data structures.

Data structures can be divided into linear data structures and non-linear data structures depending on the type of data structure. A linear data structure may be a structure in which only one piece of data is connected to another piece of data. Linear data structures may include List, Stack, Queue, and Deque. A list can refer to a set of data that has an internal order. The list may include a linked list. A linked list may be a data structure in which data is connected in such a way that each data is connected in a single line with a pointer. In a linked list, a pointer may contain connection information to the next or previous data. Depending on its form, a linked list can be expressed as a singly linked list, a doubly linked list, or a circularly linked list. A stack may be a data listing structure that allows limited access to data. A stack can be a linear data structure in which data can be processed (for example, inserted or deleted) at only one end of the data structure. Data stored in the stack may have a data structure (LIFO-Last in First Out) where the later it enters, the sooner it comes out. A queue is a data listing structure that allows limited access to data. Unlike the stack, it can be a data structure (FIFO-First in First Out) where data stored later is released later. A deck can be a data structure that can process data at both ends of the data structure.

A non-linear data structure may be a structure in which multiple pieces of data are connected behind one piece of data. Nonlinear data structures may include graph data structures. A graph data structure can be defined by vertices and edges, and an edge can include a line connecting two different vertices. Graph data structure may include a tree data structure. A tree data structure may be a data structure in which there is only one path connecting two different vertices among a plurality of vertices included in the tree. In other words, it may be a data structure that does not form a loop in the graph data structure.

Throughout this specification, computational model, neural network, network function, and neural network may be used interchangeably. Below, it is described in a unified manner as a neural network. Data structures may include neural networks. And the data structure including the neural network may be stored in a computer-readable medium. Data structures including neural networks also include data preprocessed for processing by a neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may include a loss function for learning. A data structure containing a neural network may include any of the components disclosed above. In other words, the data structure including the neural network includes preprocessed data for processing by the neural network, data input to the neural network, weights of the neural network, hyperparameters of the neural network, data acquired from the neural network, activation functions associated with each node or layer of the neural network, neural network It may be configured to include all or any combination of the loss function for learning. In addition to the configurations described above, a data structure containing a neural network may include any other information that determines the characteristics of the neural network. Additionally, the data structure may include all types of data used or generated in the computational process of a neural network and is not limited to the above. Computer-readable media may include computer-readable recording media and/or computer-readable transmission media. A neural network can generally consist of a set of interconnected computational units, which can be referred to as nodes. These nodes may also be referred to as neurons. A neural network consists of at least one node.

The data structure may include data input to the neural network. A data structure containing data input to a neural network may be stored in a computer-readable medium. Data input to the neural network may include learning data input during the neural network learning process and/or input data input to the neural network on which training has been completed. Data input to the neural network may include data that has undergone pre-processing and/or data subject to pre-processing. Preprocessing may include a data processing process to input data into a neural network. Therefore, the data structure may include data subject to preprocessing and data generated by preprocessing. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure may include the weights of the neural network. (In this specification, weights and parameters may be used with the same meaning.) And the data structure including the weights of the neural network may be stored in a computer-readable medium. A neural network may include multiple weights. Weights may be variable and may be varied by the user or algorithm in order for the neural network to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. Based on the weight, the data value output from the output node can be determined. The above-described data structure is only an example and the present disclosure is not limited thereto.

As an example and not a limitation, the weights may include weights that are changed during the neural network learning process and/or weights for which neural network learning has been completed. Weights that change during the neural network learning process may include weights that change at the start of the learning cycle and/or weights that change during the learning cycle. Weights for which neural network training has been completed may include weights for which a learning cycle has been completed. Therefore, the data structure including the weights of the neural network may include weights that are changed during the neural network learning process and/or the data structure including the weights for which neural network learning has been completed. Therefore, the above-mentioned weights and/or combinations of each weight are included in the data structure including the weights of the neural network. The above-described data structure is only an example and the present disclosure is not limited thereto.

The data structure including the weights of the neural network may be stored in a computer-readable storage medium (e.g., memory, hard disk) after going through a serialization process. Serialization can be the process of converting a data structure into a form that can be stored on the same or a different computing device and later reorganized and used. Computing devices can transmit and receive data over a network by serializing data structures. Data structures containing the weights of a serialized neural network can be reconstructed on the same computing device or on a different computing device through deserialization. The data structure including the weights of the neural network is not limited to serialization. Furthermore, the data structure including the weights of the neural network is a data structure to increase computational efficiency while minimizing the use of computing device resources (e.g., in non-linear data structures, B-Tree, Trie, m-way search tree, AVL tree, Red-Black Tree) may be included. The foregoing is merely an example and the present disclosure is not limited thereto.

The data structure may include hyper-parameters of a neural network. And the data structure including the hyperparameters of the neural network can be stored in a computer-readable medium. A hyperparameter may be a variable that can be changed by the user. Hyperparameters include, for example, learning rate, cost function, number of learning cycle repetitions, weight initialization (e.g., setting the range of weight values subject to weight initialization), Hidden Unit. It may include a number (e.g., number of hidden layers, number of nodes in hidden layers). The above-described data structure is only an example and the present disclosure is not limited thereto.

Figure 5 is a brief, general schematic diagram of an example computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has generally been described above as being capable of being implemented by a computing device, those skilled in the art will understand that the present disclosure can be implemented in combination with computer-executable instructions and/or other program modules that can be executed on one or more computers and/or in hardware and software. It will be well known that it can be implemented as a combination.

Typically, program modules include routines, programs, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Additionally, those skilled in the art will understand that the methods of the present disclosure are applicable to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics products, etc. It will be appreciated that each of these may be implemented in other computer system configurations, including those capable of operating in conjunction with one or more associated devices.

The described embodiments of the disclosure can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any medium that can be accessed by a computer, and such computer-readable media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media refers to volatile and non-volatile media, transient and non-transitory media, removable and non-removable, implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Includes media. Computer readable storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage. This includes, but is not limited to, a device, or any other medium that can be accessed by a computer and used to store desired information.

A computer-readable transmission medium typically implements computer-readable instructions, data structures, program modules, or other data on a modulated data signal, such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal have been set or changed to encode information within the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing unit 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to system memory 1106, to processing unit 1104. Processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

System bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, peripheral bus, and local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, and EEPROM, and is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

Computer 1102 may also include an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA)—the internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes - a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM for reading the disk 1122 or reading from or writing to other high-capacity optical media such as DVDs). Hard disk drive 1114, magnetic disk drive 1116, and optical disk drive 1120 are connected to system bus 1108 by hard disk drive interface 1124, magnetic disk drive interface 1126, and optical drive interface 1128, respectively. ) can be connected to. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. For computer 1102, drive and media correspond to storing any data in a suitable digital format. Although the description of computer-readable media above refers to removable optical media such as HDDs, removable magnetic disks, and CDs or DVDs, those skilled in the art will also recognize removable optical media such as zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the example operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in drives and RAM 1112, including an operating system 1130, one or more application programs 1132, other program modules 1134, and program data 1136. All or portions of the operating system, applications, modules and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented on various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into computer 1102 through one or more wired/wireless input devices, such as a keyboard 1138 and a pointing device such as mouse 1140. Other input devices (not shown) may include microphones, IR remote controls, joysticks, game pads, stylus pens, touch screens, etc. These and other input devices are connected to the processing unit 1104 through an input device interface 1142, which is often connected to the system bus 1108, but may also include a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, It can be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also connected to system bus 1108 through an interface, such as a video adapter 1146. In addition to monitor 1144, computers typically include other peripheral output devices (not shown) such as speakers, printers, etc.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communications. Remote computer(s) 1148 may be a workstation, computing device computer, router, personal computer, portable computer, microprocessor-based entertainment device, peer device, or other conventional network node, and is generally connected to computer 1102. For simplicity, only memory storage device 1150 is shown, although it includes many or all of the components described. The logical connections depicted include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, such as a wide area network (WAN) 1154. These LAN and WAN networking environments are common in offices and companies and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, such as the Internet.

When used in a LAN networking environment, computer 1102 is connected to local network 1152 through wired and/or wireless communication network interfaces or adapters 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed thereon for communicating with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158 or be connected to a communicating computing device on the WAN 1154 or to establish communications over the WAN 1154, such as via the Internet. Have other means. Modem 1158, which may be internal or external and a wired or wireless device, is coupled to system bus 1108 via serial port interface 1142. In a networked environment, program modules described for computer 1102, or portions thereof, may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and that other means of establishing a communications link between computers may be used.

Computer 1102 may be associated with any wireless device or object deployed and operating in wireless communications, such as a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communications satellite, wirelessly detectable tag. Performs actions to communicate with any device or location and telephone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows connection to the Internet, etc. without wires. Wi-Fi is a wireless technology, like cell phones, that allows these devices, such as computers, to send and receive data indoors and outdoors, anywhere within the coverage area of a base station. Wi-Fi networks use wireless technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, the Internet, and wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz wireless bands, for example, at data rates of 11 Mbps (802.11a) or 54 Mbps (802.11b), or in products that include both bands (dual band). .

Those skilled in the art will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description include voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. It can be expressed by particles or particles, or any combination thereof.

Those skilled in the art will understand that the various illustrative logical blocks, modules, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein may be used in electronic hardware, (for convenience) It will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interoperability of hardware and software, various illustrative components, blocks, modules, circuits and steps have been described above generally with respect to their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and design constraints imposed on the overall system. A person skilled in the art of this disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CDs, DVDs, etc.), smart cards, and flash. Includes, but is not limited to, memory devices (e.g., EEPROM, cards, sticks, key drives, etc.). Additionally, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the processes presented is an example of illustrative approaches. It is to be understood that the specific order or hierarchy of steps in processes may be rearranged within the scope of the present disclosure, based on design priorities. The appended method claims present elements of the various steps in a sample order but are not meant to be limited to the particular order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments without departing from the scope of the disclosure. Thus, the present disclosure is not limited to the embodiments presented herein but is to be interpreted in the broadest scope consistent with the principles and novel features presented herein.

Claims (11)

In a method for product recommendation performed by a server,
Calculating a product scoring value of a first product group for a plurality of predetermined indices, wherein the plurality of indices include at least one of a digital index, a living index, a well-being index, a surrounding environment index, and a health index, and the first product group includes products purchased or owned by the user -;
Determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate groups, wherein the product candidate group includes at least a product of the same type as the first product group and the first product group. Includes products with links -; and
determining a final recommended product from the determined second product group using a product recommendation algorithm, wherein the product recommendation algorithm includes at least one of a collaborative filtering algorithm or a machine learning model that uses follow-up management information as a factor;
Including,
The after-management information includes first after-management sub-information determined by the user and second after-management sub-information determined by the first product group,
The first after-care sub-information includes information on the number of times the user has applied for after-care, after-care application date information, after-care application content information, user after-care cost information, remaining period of free after-care, distance to and access time from adjacent after-care centers. , contains at least one of the following follow-up evaluation information,
The second after-sales service sub-information includes after-sales management entity information corresponding to the first product group purchased or held by the user, after-sales service policy evaluation information, information on free or paid period and free repair conditions, and after-sales management information on parts. Quality assurance information, after-sales service center information including the number, location, and distribution of after-sales service centers, product after-sales service cost information regarding after-sales service cost addition system and guidance, after-sales service-related transportation information, on-site repair information, after-sales service Contains at least one of the product life cycle information according to the follow-up management period, follow-up management content, and timing for the period in which it is possible,
method.
delete delete According to claim 1,
Factors used in the collaborative filtering algorithm include user-like information including at least one of gender, age, family, occupation, residential area, level of interest, purchase level, and owned products.
method.
delete delete According to claim 1,
The first post-management sub-information has a higher user-related weight compared to the second post-management sub-information, and the second post-management sub-information has a higher product-related weight than the first post-management sub-information.
method.
According to claim 1,
The product recommendation algorithm includes a plurality of algorithms,
The plurality of algorithms include at least a collaborative filtering algorithm and a machine learning model,
method.
According to claim 8,
wherein the machine learning model is at least partially learned by a training set generated based on the collaborative filtering algorithm,
method.
A computer program stored in a computer-readable storage medium, the computer program comprising instructions for causing one or more processors to perform a method for performing object-centric data augmentation, the instructions comprising:
Calculating a product scoring value of a first product group for a plurality of predetermined indices, wherein the plurality of indices include at least one of a digital index, a living index, a well-being index, a surrounding environment index, and a health index, and the first product group includes products purchased or owned by the user -;
Determining a second product group including at least one product corresponding to the product scoring value of the first product group from among the product candidate groups, wherein the product candidate group includes at least a product of the same type as the first product group and the first product group. Includes products with links -; and
determining a final recommended product from the determined second product group using a product recommendation algorithm, wherein the product recommendation algorithm includes at least one of a collaborative filtering algorithm or a machine learning model that uses follow-up management information as a factor;
Including,
The after-management information includes first after-management sub-information determined by the user and second after-management sub-information determined by the first product group,
The first after-care sub-information includes information on the number of times the user has applied for after-care, after-care application date information, after-care application content information, user after-care cost information, remaining period of free after-care, distance to and access time from adjacent after-care centers. , contains at least one of the following follow-up evaluation information,
The second after-sales service sub-information includes after-sales management entity information corresponding to the first product group purchased or held by the user, after-sales service policy evaluation information, information on free or paid period and free repair conditions, and after-sales management information on parts. Quality assurance information, after-sales service center information including the number, location, and distribution of after-sales service centers, product after-sales service cost information regarding after-sales service cost addition system and guidance, after-sales service-related transportation information, on-site repair information, after-sales service Contains at least one of the product life cycle information according to the follow-up management period, follow-up management content, and timing for the period in which it is possible,
A computer program stored on a computer-readable storage medium.
In a server performing a method for product recommendation, the server includes: memory; and processor; , wherein the processor:
Calculate a product scoring value of a first product group for a plurality of predetermined indices, wherein the plurality of indices include at least one of a digital index, a living index, a well-being index, an environment index, and a health index, and the first product group includes: Contains products purchased or owned by the user -,
Among the product candidate groups, determine a second product group including at least one product corresponding to the product scoring value of the first product group, and the product candidate group is at least the same type of product as the first product group and has connectivity with the first product group. Includes products with -, and
Determine a final recommended product from the determined second product group using a product recommendation algorithm, wherein the product recommendation algorithm includes at least one of a collaborative filtering algorithm or a machine learning model that uses follow-up management information as a factor,
The after-management information includes first after-management sub-information determined by the user and second after-management sub-information determined by the first product group,
The first after-care sub-information includes information on the number of times the user has applied for after-care, after-care application date information, after-care application content information, user after-care cost information, remaining period of free after-care, distance to and access time from adjacent after-care centers. , contains at least one of the following follow-up evaluation information,
The second after-sales service sub-information includes after-sales management entity information corresponding to the first product group purchased or held by the user, after-sales service policy evaluation information, information on free or paid period and free repair conditions, and after-sales management information on parts. Quality assurance information, after-sales service center information including the number, location, and distribution of after-sales service centers, product after-sales service cost information regarding after-sales service cost addition system and guidance, after-sales service-related transportation information, on-site repair information, after-sales service Contains at least one of the product life cycle information according to the follow-up management period, follow-up management content, and timing for the period in which it is possible,
server.