# Interaction Design of Mobile-Based Product Display System

Authors: Ying Xu
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Merchandise display design is a type of targeted design that involves the planning and implementation of merchandising, product display, visual communication, and the display of the retail environment in order to increase sales and suit the needs of both producers and customers. In this paper, we propose a mobile-based merchandise display system for the needs of merchandise display and virtual reality shopping in e-commerce. First, this paper analyzes the characteristics of several VR technologies and applies the WebVR 3D interaction engine to realize the online 3D virtual display of real-world and physical products to provide real-time interaction. Furthermore, this paper proposes a behavioral delay shared network model that fully combines historical browsing information to make accurate browsing recommendations to users. For computing the user’s interest representation, the model provides behavioral delay gated recurrent neural units. Finally, to provide a solution for mobile e-commerce apps, the behavioral delayed shared network model is implemented into the display system’s recommendation module.


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## Body

## 1. Introduction

The Internet in China has advanced at a rapid pace in the twenty-first century, and more and more people are incorporating new Internet technologies into their daily lives, particularly those that are closely tied to them, as the country’s population grows. Commodity marketing benefits from the Internet’s ability to incorporate a range of media information types such as text, audio, video, and animation [1, 2]. This allows for more vivid information display and more convenient information reading in the field of commodity marketing. This technology also makes it possible to trade on an online platform, respond to consumer information requests, and perform a variety of other duties, all of which considerably simplify communication between organizations, enterprises, and customers. As a result, prominent goods merchants have begun to develop their own retail display network systems in order to increase the exposure of their products. Merchandise display design has now advanced to the level of a corporate strategy. The added value of a company’s image and products, as well as the most vital business activities, are increased through the use of merchandise display and design [3–5]. When facing this new form of competition, we can only win the trust of consumers by precisely capturing market information and paying attention to popular fashion. Only in this way can we survive and flourish [6].Virtual reality, or VR for short, is a computer technology that simulates the actual world and generates corresponding colorful and realistic 3D virtual scenes with the use of input and output devices. Virtual reality is becoming increasingly popular. In order for virtual reality technology to work, it requires the support of both hardware and software technologies. Hardware technology is supported by data acquisition equipment, which allows for the collection of a variety of real physical movements and other data, which can then be used to create a variety of immersive auditory, visual, and tactile effects. Software technology is supported by relevant software, which can generate three-dimensional space and control the behavior of the scene, as well as human-computer interaction [7–11].With the rapid development of e-commerce platforms, the number of products has increased dramatically, making it difficult for users to find products that meet their needs among a large number of products. As a result, platform product sales have increased only slowly, despite the rapid development of e-commerce platforms [12, 13]. The recommendation system provides customers with individualized product recommendations and can actively push interesting products to the front of the sales funnel, resulting in an increase in conversion income for e-commerce sites [14].In recent years, the study of developing recommendation models based on neural networks has progressed from a niche area of study to a major academic focus. Some researchers merged the linear model and the depth model to develop the Wide Deep model, which overcomes the challenge of mixing low-order features with high-order features by combining the two models together. Deep neural networks were used in the literature [15] to construct recall and ranking models by deep extraction of embedded representations of users’ historical behaviors, respectively, which solved the real-time problem of large-scale recommendations [16–22]. For the insufficiency of combined features in the fully connected layer, some other scholars proposed a product-based recommendation model, in which the input features are passed through the product layer to obtain a higher-order combined representation, which improves the performance of click-through rate prediction even further. In contrast to this, the methods described above that combine features rarely take into account the relationship between user behavior sequences, and it is difficult to ensure that the user and product representations of the model are represented in a uniform space, which in turn causes the problem of mismatch between user interests and recommended products, resulting in a low click-through rate for recommended products [18–22].For example, in e-commerce platforms, users with varying levels of activity may exhibit varying purchase patterns, which has a direct impact on the accuracy and diversity of product recommendations. It is still very difficult to incorporate user activity levels into the temporal model in order to improve recommendation performance; however, this is becoming increasingly feasible. In large-scale recommendation scenarios, however, the mapping of user and product representations to a unified space is critical because it is necessary to improve recommendation speed by precalculating the similarity between the user vector and the product vector in order to obtain candidate products. Because the mapping of user representation and product representation to a unified space must be improved, it is critical to further improve this mapping [23, 24].Specifically, this paper proposes the Behavior Delay Shared Network, which is a model based on the analysis of different user purchase patterns. It also proposes behavioral delay gated recurrent neural units and includes the analysis of user behavioral activity as an influencing factor in the calculation process of the user browsing sequence state representation. Furthermore, to improve the recommendation effectiveness and interpretability of the model, this work merges user representation and product representation into a unified space. Furthermore, the user activity factor is introduced initially during the process of updating the state value of GRU in order to clarify how much of the previous state and current state should be retained, and the temporal model of user history behavior is created by BDGRU in order to extract the temporal characteristics of user history behavior [25, 26]. Once this has been accomplished, the user and product representations are further extracted from the higher-order user and product representations using a shared network. This ensures that the user and product representations can converge into a uniform space and increases the interpretability of the model. In the end, the BDSN model is intended to be used in the implementation of tailored product recommendations, with the accuracy and interpretability of the model having been tested on a publicly available dataset. The model is then applied to the suggestion module of the mobile-based product display system, which is the final step [27–30].The research is organized as follows: the related theory is presented in Section2. Section 3 analyzes the mobile-based product display system interaction design. The experiment and its results are discussed in Section 4 of the study. Finally, the research job is completed in Section 5.


## 2. Related Theory

In this part, we studied the relationship between merchandise display design and consumption and commodity display design guided by consumer demand.


### 2.1. Relationship between Merchandise Display Design and Consumption

Information transmission is the core meaning of display, which is a planned behavior on the part of the observer. Generally speaking, the commodity display design refers to the design of the commodity display environment, which results in the display design having a well-defined service object, which includes commodities. It is the major body of commodity display design that takes commodities as its starting point, and it provides an environment for the communication of commodity information through display design, in order to effectively transmit information and achieve various communication goals.Commodity display design is within the topic of commercial display design, and the goal of its creation is to encourage consumers to purchase products. In line with the demand for consumer upgrading, the consumer-centric aspect of item display design is appropriate. Consumer demand increases over time, resulting in a progressive growth in the variety of items available. The form of merchandise display design plays an essential part in stimulating consumption, as depicted in Figure1.Figure 1
The relationship between commodity, display design, and consumption.Merchandise display is a crucial link between merchandising and consumer consumption since it allows for easy identification of products. When it comes to merchandising, applying display design means using various media to express a visual effect in the merchandising space, in order to achieve the goal of conveying merchandise information, to provide a comfortable consumption space for consumers, to create an interaction between merchandise and consumers, and to bring a unique experience to the consumer. In the merchandising area, the display design is utilized to produce a visual effect via the use of various media with the goal of delivering product information and providing a comfortable environment for consumers to consume.The goal of consumption upgrading is to have a more balanced relationship between consumer demand and consumption production. Consumption upgrading refers to the growth of the scope and quality of people’s consumption lives, as well as the development of new consumption practices and methodologies. It is divided into four categories: increasing the physical quantity of consumption, consuming higher-quality items and services, adding new consumption contents, and utilizing new consumption forms. The primary direction of consumption upgrading varies depending on where you are in the process. People’s consumption psychology is changing dramatically in the setting of the modern period of consumption upgradation. Customers no longer meet their basic material needs but instead pay more attention to the inner feelings that the goods bring about in them, in pursuit of a spiritual realm of pleasure, during the process of commodity consumption; they no longer only focus on the quality of goods but also on the inner feelings that the goods bring about in themselves. As a result of this shift in consumer psychology, the concept of consumption levels has been developed. There are three levels of consumption upgrade currently underway in China, and the consumption structure of the population is being upgraded in accordance with nondurable products consumption. Consumption of durable commodities and consumption of services are both in transition. The consumer’s desire for commodities can be separated into hierarchical relationships at each of these three levels of the commodity consumption demand hierarchy. The demand for people’s consumption can be split into three categories based on their distinct consumption goals and the ease with which they can achieve them. These categories include demand consumption, quality consumption, and character consumption. Following this logic, the types of consumer supply goods display design may be classified into three categories: the fundamental good display design, the brand-type good display design, and the leading good display design. The shift between each level represents not only the process of customer upgrading but also the process of increasing the overall quality of commodity display design and layout. Only the customer desire and the form of commodities display design are compatible with meeting the fundamental requirements of consumer upgrading, according to the study.


### 2.2. Commodity Display Design Guided by Consumer Demand

Consumers who desire to live a more pleasant life while staying within their consumption capacity can upgrade their consumption habits. In order to provide consumers with the best possible consumer experience, consumer demand must be aligned with the form of commodity display design. Consumer demand consumes primarily material needs and falls under the category of frugal consumption; consumer character consumes primarily spiritual needs and falls under the category of service consumption; consumer quality consumption is a compromise between consumer demand and consumer character consumption, primarily focusing on product consumption. The form of commodities display design and the supply of items are complementary to one another; display design gives a supply of demand solutions to fulfill a variety of customer requirements.Demand consumption is primarily driven by consumers’ basic material necessities; yet, consumers’ demand for commodities itself is more deliberate, with the top goal being to meet the practical value of the goods. In response to demand consumption, the fundamental display design focuses on the arrangement of the items themselves, which is known as the budding stage of design, and it arranges the goods in an ordered and neat manner in order to present the goods in their most effective manner for customers. Consumers that spend for the sole purpose of satisfying demand are primarily found in low-income areas and are primarily composed of people with low income.When it comes to quality consumption, the focus is on the products’ quality and customers set greater expectations for them. The display form implies that the products are being promoted, and it does so on purpose. A reduction in the number of goods presented and the usage of display auxiliary equipment are two factors of a promotional display design that are influenced by quality. The usage of display design is important in the case of products that have the same quantity and quality, in order to achieve the goal of capturing the attention and confidence of consumers. Consumers’ attention is drawn to goods by using eye-catching visual effects created by using decorative materials, color matching, lighting arrangements, and other techniques to increase the visual effect, stimulate the senses, and then encourage them to purchase the goods that have been displayed on a regular basis. People’s income levels are rising in tandem with the ongoing development of the economic level, and the economic level of consumers is now able to support consumption that is above and beyond the bare essentials of existence. The growth of the economy has also increased the quantity of commodities available; the same things are now offered under a variety of brand names, allowing consumers to be more selective. Children and teenagers in the twenty-first century have improved their comprehension and changed their consumption conceptions, putting a higher value on product quality and being more easily influenced by brand power.Apart from the use and service values of commodities, consumers place an increased emphasis on the personalization and experience value of products. A potential goal of the display area is to communicate to customers about the corporate image, as well as the values and life philosophy contained in the brand image. Because of the influence of character consumption on display design forms, the ultimate goal of the leading display design form has gone beyond the commodity itself; it has become a conversation between display design and people that has features of technology and humanization. Having a sense of the future and being forward-looking, this type of display design is effective.The intuitive embodiment of the current third consumer upgrade, which includes not only the development of consumption ability but also the adjustment of consumption method and consumption idea, is a character’s consumption demand. The bulk of this consumer group are members of society’s elite; among them are scientists who innovate and produce, heads of social businesses, specialists and scholars with enormous knowledge stores, and so on. This consumer group is already comprised of members of the elite.


## 2.1. Relationship between Merchandise Display Design and Consumption

Information transmission is the core meaning of display, which is a planned behavior on the part of the observer. Generally speaking, the commodity display design refers to the design of the commodity display environment, which results in the display design having a well-defined service object, which includes commodities. It is the major body of commodity display design that takes commodities as its starting point, and it provides an environment for the communication of commodity information through display design, in order to effectively transmit information and achieve various communication goals.Commodity display design is within the topic of commercial display design, and the goal of its creation is to encourage consumers to purchase products. In line with the demand for consumer upgrading, the consumer-centric aspect of item display design is appropriate. Consumer demand increases over time, resulting in a progressive growth in the variety of items available. The form of merchandise display design plays an essential part in stimulating consumption, as depicted in Figure1.Figure 1
The relationship between commodity, display design, and consumption.Merchandise display is a crucial link between merchandising and consumer consumption since it allows for easy identification of products. When it comes to merchandising, applying display design means using various media to express a visual effect in the merchandising space, in order to achieve the goal of conveying merchandise information, to provide a comfortable consumption space for consumers, to create an interaction between merchandise and consumers, and to bring a unique experience to the consumer. In the merchandising area, the display design is utilized to produce a visual effect via the use of various media with the goal of delivering product information and providing a comfortable environment for consumers to consume.The goal of consumption upgrading is to have a more balanced relationship between consumer demand and consumption production. Consumption upgrading refers to the growth of the scope and quality of people’s consumption lives, as well as the development of new consumption practices and methodologies. It is divided into four categories: increasing the physical quantity of consumption, consuming higher-quality items and services, adding new consumption contents, and utilizing new consumption forms. The primary direction of consumption upgrading varies depending on where you are in the process. People’s consumption psychology is changing dramatically in the setting of the modern period of consumption upgradation. Customers no longer meet their basic material needs but instead pay more attention to the inner feelings that the goods bring about in them, in pursuit of a spiritual realm of pleasure, during the process of commodity consumption; they no longer only focus on the quality of goods but also on the inner feelings that the goods bring about in themselves. As a result of this shift in consumer psychology, the concept of consumption levels has been developed. There are three levels of consumption upgrade currently underway in China, and the consumption structure of the population is being upgraded in accordance with nondurable products consumption. Consumption of durable commodities and consumption of services are both in transition. The consumer’s desire for commodities can be separated into hierarchical relationships at each of these three levels of the commodity consumption demand hierarchy. The demand for people’s consumption can be split into three categories based on their distinct consumption goals and the ease with which they can achieve them. These categories include demand consumption, quality consumption, and character consumption. Following this logic, the types of consumer supply goods display design may be classified into three categories: the fundamental good display design, the brand-type good display design, and the leading good display design. The shift between each level represents not only the process of customer upgrading but also the process of increasing the overall quality of commodity display design and layout. Only the customer desire and the form of commodities display design are compatible with meeting the fundamental requirements of consumer upgrading, according to the study.


## 2.2. Commodity Display Design Guided by Consumer Demand

Consumers who desire to live a more pleasant life while staying within their consumption capacity can upgrade their consumption habits. In order to provide consumers with the best possible consumer experience, consumer demand must be aligned with the form of commodity display design. Consumer demand consumes primarily material needs and falls under the category of frugal consumption; consumer character consumes primarily spiritual needs and falls under the category of service consumption; consumer quality consumption is a compromise between consumer demand and consumer character consumption, primarily focusing on product consumption. The form of commodities display design and the supply of items are complementary to one another; display design gives a supply of demand solutions to fulfill a variety of customer requirements.Demand consumption is primarily driven by consumers’ basic material necessities; yet, consumers’ demand for commodities itself is more deliberate, with the top goal being to meet the practical value of the goods. In response to demand consumption, the fundamental display design focuses on the arrangement of the items themselves, which is known as the budding stage of design, and it arranges the goods in an ordered and neat manner in order to present the goods in their most effective manner for customers. Consumers that spend for the sole purpose of satisfying demand are primarily found in low-income areas and are primarily composed of people with low income.When it comes to quality consumption, the focus is on the products’ quality and customers set greater expectations for them. The display form implies that the products are being promoted, and it does so on purpose. A reduction in the number of goods presented and the usage of display auxiliary equipment are two factors of a promotional display design that are influenced by quality. The usage of display design is important in the case of products that have the same quantity and quality, in order to achieve the goal of capturing the attention and confidence of consumers. Consumers’ attention is drawn to goods by using eye-catching visual effects created by using decorative materials, color matching, lighting arrangements, and other techniques to increase the visual effect, stimulate the senses, and then encourage them to purchase the goods that have been displayed on a regular basis. People’s income levels are rising in tandem with the ongoing development of the economic level, and the economic level of consumers is now able to support consumption that is above and beyond the bare essentials of existence. The growth of the economy has also increased the quantity of commodities available; the same things are now offered under a variety of brand names, allowing consumers to be more selective. Children and teenagers in the twenty-first century have improved their comprehension and changed their consumption conceptions, putting a higher value on product quality and being more easily influenced by brand power.Apart from the use and service values of commodities, consumers place an increased emphasis on the personalization and experience value of products. A potential goal of the display area is to communicate to customers about the corporate image, as well as the values and life philosophy contained in the brand image. Because of the influence of character consumption on display design forms, the ultimate goal of the leading display design form has gone beyond the commodity itself; it has become a conversation between display design and people that has features of technology and humanization. Having a sense of the future and being forward-looking, this type of display design is effective.The intuitive embodiment of the current third consumer upgrade, which includes not only the development of consumption ability but also the adjustment of consumption method and consumption idea, is a character’s consumption demand. The bulk of this consumer group are members of society’s elite; among them are scientists who innovate and produce, heads of social businesses, specialists and scholars with enormous knowledge stores, and so on. This consumer group is already comprised of members of the elite.


## 3. Mobile-Based Product Display System Interaction Design

In this section, we defined the system function, database design, and system development and implementation.


### 3.1. System Function

Providing acceptable and accurate product recommendations, as well as a friendly display, in accordance with the user’s registration information, is the most crucial thing to do. General users, merchant users, and system administrators are the three sorts of roles in the system. Different roles have a variety of privileges and responsibilities. Detailed descriptions of the specifics can be found hereunder.


#### 3.1.1. The General Public

(1) Registration and Login/Logout for Users. This provides users with the ability to register and login to their accounts. Individuals must enter information about their industry, areas of interest, and subjects they want to learn about while enrolling. The system will provide each individual user with a distributed product promotion function based on this information.(2) Product Display. This provides users with the ability to view products on a screen. There are two types of product presentations. Product display can be divided into two types: product display based on the user’s tags and product display based on a search.(3) Product Search. Product search provides users with the ability to search for specific products. A range of fields is required for this function in order to satisfy the search for products, for example, product name, product category, and product brand.(4) Customer Service. In addition, because the presentation of items does not entail payment, but rather merely provides a recommendation and a display platform, the customer service system is not required. As a result, customer assistance must be offered prior to the distribution of goods in order to facilitate contact between users and merchants.(5) Editing of User Information. This includes editing of user information, that is, the amendment of personally identifiable information.


#### 3.1.2. Merchant Management Professionals

In addition to the functions performed by regular users, the system also has the following common operation functions: publishing, editing, logical deletion, search, and search of product information.


#### 3.1.3. The System Administrator

The system administrator is subdivided into three categories: management of all products, administration of users, and administration of merchant users’ personal information.The management of all goods, in particular, relates to the management of all merchants’ public product information, which is divided into two groups. The management of registered users relates to the administration of personal information for both general and merchant users who have created accounts with the system. The administration of system activities such as the layout of the system, the main color, and the recommendation to rotate the home page is referred to as system settings management.


### 3.2. Database Design

The system’s data is kept in MySQL 6.0, where it is organized into the following five tables:(1)
Product information table contains the most basic information about the items in the cart, for example, ID or product name.(2)
The registrant’s information, such as his or her name, account number, password, permission, profile, and other pertinent data, is stored in the registered user’s database, for example, the user’s keywords and tags.(3)
Product category table stores the ID and name of each product category, among other information.(4)
Offline suggestion table is maintained, which stores the offline recommendation list made by each user based on their individual history of browsing.(5)
The system maintains a real-time suggestion list, which is saved in Redis and updated in real time. The offline suggestion list is stored in a MySQL database and is regularly updated.


### 3.3. System Development and Implementation

The system is divided into six modules: a product management module, a category management module, a product and recommendation display module, a user information management module, a system management module, and a keyword management module. The product management module is divided into two parts: product and recommendation display module.


#### 3.3.1. System Common Part Implementation

The system is organized into five packages: cn.mymobileshop.message, cn.mymobileshop. product, cn.mymobileshop.tags, en.mymobileshop.interactions, and cn.mymobileshop.os. The packages are organized into four categories: cn.mymobileshop.message, cn.mymobileshop.product, cn.mymobileshop.tags, and en.mymobileshop.interactions. Each table in the database is represented by an object class, which is stored in the entity package. The interface, which is responsible for connecting with the database, is stored in the Dao package. The Service package contains the service specification interface and its associated service package. In the Service package, there is a service specification interface and an implements class that are responsible for the primary functionalities and customised business logic, respectively.


#### 3.3.2. Recommendation Page Implementation

A real-time suggestion page and an offline recommendation page are both available on the recommendation page. Real-time recommendations are provided. Kafka’s user behavior data (user tags, user’s recent browsing (within one day), and other behaviors) is monitored, real-time calculations are performed, and the results are written back to Redis. Offline recommendations are calculated based on the user’s recent usage footprint and are not affected by real-time recommendations. And, at a qualitative time interval, the offline calculation is carried out by running the code set that has been predefined in advance. Following that, the results of the suggestion calculations are saved into the MySQL database for future reference.The recommendation method described in this research for use in machine learning applications is the BDSP algorithm (Behavior Delayed Shared Parameter). To a certain extent, the frequency of user behavior (i.e., user activity) determines the present projected behavior of users. User activity is not taken into account by traditional GRU when updating the entire user history behavior state, but the BDSPGRU method proposed in this paper incorporates user activity into the update phase of the hidden state, specifying how much information from the previous sequence and the current moment should be kept, and the BDSPGRU structure is described in detail. As depicted in Figure2, the cell update gate is determined using the following formula:(1)ht−1′=1+dtt∘ht−1,ht−1′=1−dtt∘h˜t,ht−1′=1−ut−1∘ht−1′+ut∘h˜t−1′,where ht−1 and h˜t are the hidden state values at moment t−1 and at moment t, respectively, in the traditional GRU and dtt denotes the activity factor at moment t. The user activity in this paper is in vector form and is calculated as follows:(2)dt=softmoaxΔT,where ΔT∈LT is the time interval between the user’s behavior at moment t and the behavior at moment t−1 and LT is the length of the user’s historical behavior. The results of (2) show that frequent browsing behavior will result in the retention of more t-moment states and the recommendation of similar goods; however, infrequent browsing behavior will result in the retention of more states before the t-moment and the recommendation of a greater variety of products.Figure 2
The structure of BDSPGRU.In order to improve the intrinsic association between user representation and commodity representation, this paper proposes a shared representation layer, which allows the final user representation and commodity representation to converge to a unified space as a result of the shared representation layer. Prior to the MLP, a shared network layer is created in order to obtain representations of the users and commodities. Firstly, the splicing features of users and goods are mapped into user and goods vectors of the same sizevp,vs∈nc by a single-layer network, which is calculated as follows:(3)vp=Wpip+bp,vs=Wsis+bs.The final state output of the BDGRU on the user side is denoted byip, and the commodity embedding representation on the target commodity side is denoted by is. Wp∈Rnc×np and Ws∈Rnc×ns denote the weight matrices of the network on the user and commodity sides, respectively, while nc,np, and ns denote the number of network hidden layer units, the user-side network input feature size, and the commodity-side network input feature size, respectively. Once this is done, the user and commodity vectors are transferred over the shared network layer to obtain the final user and commodity representations, which are represented by the following formulas:(4)vp′=Wcvp+bc.(5)vs′=Wcvs+bc.The calculation function between layers is(6)fz=F−1Fk⊙Fα.Users’ and candidate products’ basic characteristics are first processed through embedding representation, and then, the feature representation, which includes both user- and product-side characteristics, is obtained through splicing operation. User-side characteristics are extracted using BDSTGRU for temporal characteristics, and then, user and product characteristics are used to obtain higher-order unified representation through a shared parameter network. Finally, the product suggestion is achieved through the calculation of the user click-through rate for the product, and the predicted click-through rate is determined as follows:(7)y^c=WM⋯W1Concatvp′,vs′+b1⋯+bM.And, the loss function is(8)L=−1N∑x,yNy⋅log1−y^cx+1−y⋅log1−y^cx.


## 3.1. System Function

Providing acceptable and accurate product recommendations, as well as a friendly display, in accordance with the user’s registration information, is the most crucial thing to do. General users, merchant users, and system administrators are the three sorts of roles in the system. Different roles have a variety of privileges and responsibilities. Detailed descriptions of the specifics can be found hereunder.


### 3.1.1. The General Public

(1) Registration and Login/Logout for Users. This provides users with the ability to register and login to their accounts. Individuals must enter information about their industry, areas of interest, and subjects they want to learn about while enrolling. The system will provide each individual user with a distributed product promotion function based on this information.(2) Product Display. This provides users with the ability to view products on a screen. There are two types of product presentations. Product display can be divided into two types: product display based on the user’s tags and product display based on a search.(3) Product Search. Product search provides users with the ability to search for specific products. A range of fields is required for this function in order to satisfy the search for products, for example, product name, product category, and product brand.(4) Customer Service. In addition, because the presentation of items does not entail payment, but rather merely provides a recommendation and a display platform, the customer service system is not required. As a result, customer assistance must be offered prior to the distribution of goods in order to facilitate contact between users and merchants.(5) Editing of User Information. This includes editing of user information, that is, the amendment of personally identifiable information.


### 3.1.2. Merchant Management Professionals

In addition to the functions performed by regular users, the system also has the following common operation functions: publishing, editing, logical deletion, search, and search of product information.


### 3.1.3. The System Administrator

The system administrator is subdivided into three categories: management of all products, administration of users, and administration of merchant users’ personal information.The management of all goods, in particular, relates to the management of all merchants’ public product information, which is divided into two groups. The management of registered users relates to the administration of personal information for both general and merchant users who have created accounts with the system. The administration of system activities such as the layout of the system, the main color, and the recommendation to rotate the home page is referred to as system settings management.


## 3.1.1. The General Public

(1) Registration and Login/Logout for Users. This provides users with the ability to register and login to their accounts. Individuals must enter information about their industry, areas of interest, and subjects they want to learn about while enrolling. The system will provide each individual user with a distributed product promotion function based on this information.(2) Product Display. This provides users with the ability to view products on a screen. There are two types of product presentations. Product display can be divided into two types: product display based on the user’s tags and product display based on a search.(3) Product Search. Product search provides users with the ability to search for specific products. A range of fields is required for this function in order to satisfy the search for products, for example, product name, product category, and product brand.(4) Customer Service. In addition, because the presentation of items does not entail payment, but rather merely provides a recommendation and a display platform, the customer service system is not required. As a result, customer assistance must be offered prior to the distribution of goods in order to facilitate contact between users and merchants.(5) Editing of User Information. This includes editing of user information, that is, the amendment of personally identifiable information.


## 3.1.2. Merchant Management Professionals

In addition to the functions performed by regular users, the system also has the following common operation functions: publishing, editing, logical deletion, search, and search of product information.


## 3.1.3. The System Administrator

The system administrator is subdivided into three categories: management of all products, administration of users, and administration of merchant users’ personal information.The management of all goods, in particular, relates to the management of all merchants’ public product information, which is divided into two groups. The management of registered users relates to the administration of personal information for both general and merchant users who have created accounts with the system. The administration of system activities such as the layout of the system, the main color, and the recommendation to rotate the home page is referred to as system settings management.


## 3.2. Database Design

The system’s data is kept in MySQL 6.0, where it is organized into the following five tables:(1)
Product information table contains the most basic information about the items in the cart, for example, ID or product name.(2)
The registrant’s information, such as his or her name, account number, password, permission, profile, and other pertinent data, is stored in the registered user’s database, for example, the user’s keywords and tags.(3)
Product category table stores the ID and name of each product category, among other information.(4)
Offline suggestion table is maintained, which stores the offline recommendation list made by each user based on their individual history of browsing.(5)
The system maintains a real-time suggestion list, which is saved in Redis and updated in real time. The offline suggestion list is stored in a MySQL database and is regularly updated.


## 3.3. System Development and Implementation

The system is divided into six modules: a product management module, a category management module, a product and recommendation display module, a user information management module, a system management module, and a keyword management module. The product management module is divided into two parts: product and recommendation display module.


### 3.3.1. System Common Part Implementation

The system is organized into five packages: cn.mymobileshop.message, cn.mymobileshop. product, cn.mymobileshop.tags, en.mymobileshop.interactions, and cn.mymobileshop.os. The packages are organized into four categories: cn.mymobileshop.message, cn.mymobileshop.product, cn.mymobileshop.tags, and en.mymobileshop.interactions. Each table in the database is represented by an object class, which is stored in the entity package. The interface, which is responsible for connecting with the database, is stored in the Dao package. The Service package contains the service specification interface and its associated service package. In the Service package, there is a service specification interface and an implements class that are responsible for the primary functionalities and customised business logic, respectively.


### 3.3.2. Recommendation Page Implementation

A real-time suggestion page and an offline recommendation page are both available on the recommendation page. Real-time recommendations are provided. Kafka’s user behavior data (user tags, user’s recent browsing (within one day), and other behaviors) is monitored, real-time calculations are performed, and the results are written back to Redis. Offline recommendations are calculated based on the user’s recent usage footprint and are not affected by real-time recommendations. And, at a qualitative time interval, the offline calculation is carried out by running the code set that has been predefined in advance. Following that, the results of the suggestion calculations are saved into the MySQL database for future reference.The recommendation method described in this research for use in machine learning applications is the BDSP algorithm (Behavior Delayed Shared Parameter). To a certain extent, the frequency of user behavior (i.e., user activity) determines the present projected behavior of users. User activity is not taken into account by traditional GRU when updating the entire user history behavior state, but the BDSPGRU method proposed in this paper incorporates user activity into the update phase of the hidden state, specifying how much information from the previous sequence and the current moment should be kept, and the BDSPGRU structure is described in detail. As depicted in Figure2, the cell update gate is determined using the following formula:(1)ht−1′=1+dtt∘ht−1,ht−1′=1−dtt∘h˜t,ht−1′=1−ut−1∘ht−1′+ut∘h˜t−1′,where ht−1 and h˜t are the hidden state values at moment t−1 and at moment t, respectively, in the traditional GRU and dtt denotes the activity factor at moment t. The user activity in this paper is in vector form and is calculated as follows:(2)dt=softmoaxΔT,where ΔT∈LT is the time interval between the user’s behavior at moment t and the behavior at moment t−1 and LT is the length of the user’s historical behavior. The results of (2) show that frequent browsing behavior will result in the retention of more t-moment states and the recommendation of similar goods; however, infrequent browsing behavior will result in the retention of more states before the t-moment and the recommendation of a greater variety of products.Figure 2
The structure of BDSPGRU.In order to improve the intrinsic association between user representation and commodity representation, this paper proposes a shared representation layer, which allows the final user representation and commodity representation to converge to a unified space as a result of the shared representation layer. Prior to the MLP, a shared network layer is created in order to obtain representations of the users and commodities. Firstly, the splicing features of users and goods are mapped into user and goods vectors of the same sizevp,vs∈nc by a single-layer network, which is calculated as follows:(3)vp=Wpip+bp,vs=Wsis+bs.The final state output of the BDGRU on the user side is denoted byip, and the commodity embedding representation on the target commodity side is denoted by is. Wp∈Rnc×np and Ws∈Rnc×ns denote the weight matrices of the network on the user and commodity sides, respectively, while nc,np, and ns denote the number of network hidden layer units, the user-side network input feature size, and the commodity-side network input feature size, respectively. Once this is done, the user and commodity vectors are transferred over the shared network layer to obtain the final user and commodity representations, which are represented by the following formulas:(4)vp′=Wcvp+bc.(5)vs′=Wcvs+bc.The calculation function between layers is(6)fz=F−1Fk⊙Fα.Users’ and candidate products’ basic characteristics are first processed through embedding representation, and then, the feature representation, which includes both user- and product-side characteristics, is obtained through splicing operation. User-side characteristics are extracted using BDSTGRU for temporal characteristics, and then, user and product characteristics are used to obtain higher-order unified representation through a shared parameter network. Finally, the product suggestion is achieved through the calculation of the user click-through rate for the product, and the predicted click-through rate is determined as follows:(7)y^c=WM⋯W1Concatvp′,vs′+b1⋯+bM.And, the loss function is(8)L=−1N∑x,yNy⋅log1−y^cx+1−y⋅log1−y^cx.


## 3.3.1. System Common Part Implementation

The system is organized into five packages: cn.mymobileshop.message, cn.mymobileshop. product, cn.mymobileshop.tags, en.mymobileshop.interactions, and cn.mymobileshop.os. The packages are organized into four categories: cn.mymobileshop.message, cn.mymobileshop.product, cn.mymobileshop.tags, and en.mymobileshop.interactions. Each table in the database is represented by an object class, which is stored in the entity package. The interface, which is responsible for connecting with the database, is stored in the Dao package. The Service package contains the service specification interface and its associated service package. In the Service package, there is a service specification interface and an implements class that are responsible for the primary functionalities and customised business logic, respectively.


## 3.3.2. Recommendation Page Implementation

A real-time suggestion page and an offline recommendation page are both available on the recommendation page. Real-time recommendations are provided. Kafka’s user behavior data (user tags, user’s recent browsing (within one day), and other behaviors) is monitored, real-time calculations are performed, and the results are written back to Redis. Offline recommendations are calculated based on the user’s recent usage footprint and are not affected by real-time recommendations. And, at a qualitative time interval, the offline calculation is carried out by running the code set that has been predefined in advance. Following that, the results of the suggestion calculations are saved into the MySQL database for future reference.The recommendation method described in this research for use in machine learning applications is the BDSP algorithm (Behavior Delayed Shared Parameter). To a certain extent, the frequency of user behavior (i.e., user activity) determines the present projected behavior of users. User activity is not taken into account by traditional GRU when updating the entire user history behavior state, but the BDSPGRU method proposed in this paper incorporates user activity into the update phase of the hidden state, specifying how much information from the previous sequence and the current moment should be kept, and the BDSPGRU structure is described in detail. As depicted in Figure2, the cell update gate is determined using the following formula:(1)ht−1′=1+dtt∘ht−1,ht−1′=1−dtt∘h˜t,ht−1′=1−ut−1∘ht−1′+ut∘h˜t−1′,where ht−1 and h˜t are the hidden state values at moment t−1 and at moment t, respectively, in the traditional GRU and dtt denotes the activity factor at moment t. The user activity in this paper is in vector form and is calculated as follows:(2)dt=softmoaxΔT,where ΔT∈LT is the time interval between the user’s behavior at moment t and the behavior at moment t−1 and LT is the length of the user’s historical behavior. The results of (2) show that frequent browsing behavior will result in the retention of more t-moment states and the recommendation of similar goods; however, infrequent browsing behavior will result in the retention of more states before the t-moment and the recommendation of a greater variety of products.Figure 2
The structure of BDSPGRU.In order to improve the intrinsic association between user representation and commodity representation, this paper proposes a shared representation layer, which allows the final user representation and commodity representation to converge to a unified space as a result of the shared representation layer. Prior to the MLP, a shared network layer is created in order to obtain representations of the users and commodities. Firstly, the splicing features of users and goods are mapped into user and goods vectors of the same sizevp,vs∈nc by a single-layer network, which is calculated as follows:(3)vp=Wpip+bp,vs=Wsis+bs.The final state output of the BDGRU on the user side is denoted byip, and the commodity embedding representation on the target commodity side is denoted by is. Wp∈Rnc×np and Ws∈Rnc×ns denote the weight matrices of the network on the user and commodity sides, respectively, while nc,np, and ns denote the number of network hidden layer units, the user-side network input feature size, and the commodity-side network input feature size, respectively. Once this is done, the user and commodity vectors are transferred over the shared network layer to obtain the final user and commodity representations, which are represented by the following formulas:(4)vp′=Wcvp+bc.(5)vs′=Wcvs+bc.The calculation function between layers is(6)fz=F−1Fk⊙Fα.Users’ and candidate products’ basic characteristics are first processed through embedding representation, and then, the feature representation, which includes both user- and product-side characteristics, is obtained through splicing operation. User-side characteristics are extracted using BDSTGRU for temporal characteristics, and then, user and product characteristics are used to obtain higher-order unified representation through a shared parameter network. Finally, the product suggestion is achieved through the calculation of the user click-through rate for the product, and the predicted click-through rate is determined as follows:(7)y^c=WM⋯W1Concatvp′,vs′+b1⋯+bM.And, the loss function is(8)L=−1N∑x,yNy⋅log1−y^cx+1−y⋅log1−y^cx.


## 4. Experiment Results

In order to verify the predicted performance of the click-through rate of the proposed recommendation method in the interactive system of the mobile-based product display system, this paper uses AUC and RelaImpr evaluation metrics.To verify the prediction performance of the delayed shared network, it is compared with the ADSS, GCNN, and DCNN networks and the attention interest network TCNN. To verify that the proposed method is effective, this paper also designs a variant network of BDSPGRU, GRUNN, by replacing the GRU network in BDSPGRU with the conventional GRU and keeping the other structures unchanged.As shown in Figure3, the computed results of the hit rate generated by each iteration of the validation set, as well as changes in the loss value throughout model training, were gathered in order to showcase the performance of the model during training. When the training set data are plotted as a line graph, the horizontal axis represents the number of iterations and the vertical axis represents the AUC results of the validation set data. From this graph, it can be seen that the final AUC index of the algorithm in this paper can reach more than 0.75 and remain stable and the AUC values of the other models in the validation set data are ultimately lower than those of the algorithm in this paper.Figure 3
Comparison of AUC value.The loss value changes during the model training process, as illustrated in Figure4. After 10 iterations, the loss value of the algorithm shown in this study is nearly zero, which is significantly lower than the loss value of other models presented in the literature.Figure 4
Comparison of loss.The abovementioned findings demonstrate that the suggested model is capable of maintaining high performance throughout the training period. The network parameters converge slowly in the early stages of training due to the perturbation of the user activity factor in the early stages of training, but after about 20 iterations of training, the AUC index of other network models starts to decrease or stops increasing due to the overfitting problem, whereas the AUC index of the delayed sharing network proposed in this paper is optimal when compared to other network models and no overfitting problem occurs.For the purpose of illustrating the effect of BDSTGRU on the final state value output, the final state values of BDSTGRU and traditional GRU were numerically counted after 20 model training iterations, and the final state value statistics of BDSTGRU and traditional GRU are depicted in Figures5 and 6, respectively, after 20 model training iterations. This shows that most data frequencies in BDSTGRU are below 500, which is more widely distributed and clearly obeys central limit theory, while most data frequencies in BDSTGRU are above 500, which is more concentrated and obeys the distribution characteristics of less in the middle and more at the ends, as shown in the graphs.Figure 5
The final state value of our model.Figure 6
The final state value of the GRU model.Results show that the introduction of a delay factor into BDSTGRU can improve the user sequence representation effect and make the user representation more diverse, thereby improving the overall performance of the network, whereas the traditional GRU output is mostly concentrated, which only has the effect of reversing or keeping the same for subsequent network calculations, and the expression ability is not as good as the BDSTGRU output.


## 5. Conclusion

In this paper, we propose a mobile-based merchandise display system for the needs of merchandise display and virtual reality shopping in e-commerce. This study proposes a mobile-based product display system to fulfil the needs of producers and customers in order to better showcase mobile items and realize mobile video recommendation. This paper proposes a behavior-sharing delay network for the influence of corpse activity and corpse item representation on personalized e-commerce recommendation results in the system’s recommendation module, which focuses more on recent item information for frequent users and more on previous item information for infrequent users. Using real datasets, the experiments are compared to the current state-of-the-art approaches.



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*Source: 7233677-2022-05-20.xml*