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DOI: 10.32604/cmes.2021.014347
ARTICLE
Dynamic Pricing Model of E-Commerce Platforms Based on
Deep Reinforcement Learning
Chunli Yin1,* and Jinglong Han2
1College of Economics and Administration, Tonghua Normal University, Jilin, 130000, China
2Department of Administration Section, Tonghua Normal University, Jilin, 130000, China
*Corresponding Author: Chunli Yin. Email: TH Yin@thnu.edu.cn
Received: 19 September 2020 Accepted: 12 November 2020
ABSTRACT
With the continuous development of articial intelligence technology, its application eld has gradually expanded.
To further apply the deep reinforcement learning technology to the eld of dynamic pricing, we build an intelligent
dynamic pricing system, introduce the reinforcement learning technology related to dynamic pricing, and intro-
duce existing research on the number of suppliers (single supplier and multiple suppliers), environmental models,
and selection algorithms. A two-period dynamic pricing game model is designed to assess the optimal pricing
strategy for e-commerce platforms under two market conditions and two consumer participation conditions. The
rst step is to analyze the pricing strategies of e-commerce platforms in mature markets, analyze the optimal pricing
and prots of various enterprises under dierent strategy combinations, compare dierent market equilibriums and
solve the Nash equilibrium. Then, assuming that all consumers are naive in the market, the pricing strategy of the
duopoly e-commerce platform in emerging markets is analyzed. By comparing and analyzing the optimal pricing
and total prot of each enterprise under dierent strategy combinations, the subgame rened Nash equilibrium is
solved. Finally, assuming that the market includes all experienced consumers, the pricing strategy of the duopoly
e-commerce platform in emerging markets is analyzed.
KEYWORDS
Deep reinforcement learning; e-commerce platform; dynamic evaluation; game model; pricing strategy
1 Introduction
With the development of the Internet and the popularization of e-commerce, it has become
easier for people to obtain more comprehensive information on goods and services. Changes
in the price of goods or services will also have an impact on consumers’ shopping behavior
in the shortest time, which directly affects corporate prots. To maximize efciency, companies
often adjust the prices of goods or services regularly or irregularly based on certain factors,
which is also consistent with the goal of deep reinforcement learning in the eld of articial
intelligence. The goal of deep reinforcement learning is to maximize long-term benets. Therefore,
the technical means of deep reinforcement learning can achieve the intelligent pricing of goods
This work is licensed under a Creative Commons Attribution 4.0 International License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
292 CMES, 2021, vol.127, no.1
or services. The e-commerce customer’s purchase behavior prediction makes a real-time prediction
of an online customer’s purchase tendency behavior based on the behavioral laws contained in the
consumer’s historical access click operations, server logs, browsing records and product feedback
information. Therefore, customers can recommend products, formulate marketing strategies, and
determine the purchase and shipment of platform products.
Dynamic pricing is a strategy for enterprises to dynamically adjust commodity prices based
on customer demand, their own supply capacity and other information to maximize revenues [1],
and some scholars also call it personalized pricing [2]. With the continuous development of
articial intelligence technology, increasingly more scholars have sought to use intelligent methods
to solve dynamic pricing problems. Deep reinforcement learning is one of the most widely used
technologies. It is inspired by the ability of people and animals in nature to adapt to the
environment effectively. Learning from the environment through continuous trial and error is an
important branch of machine learning. It has a very wide range of applications in the elds of
articial intelligence problem solving, multiagent control, robot control and motion planning, and
decision-making control [3,4], Learning from the environment is one of the core technologies of
intelligent system design and decision-making, and it is also a key issue in dynamic pricing in
strategy research. The development is of the Internet, increasingly erce market competition, and
the need for customer management have transformed the pricing model of commercial enterprises
from xed prices to dynamic pricing. This transformation relies heavily on the development
of the Internet, market competition, and customer management needs. Dynamic pricing in an
e-commerce environment is based on the customer’s value of a subproduct or service [5,6]anda
dynamic price adjustment strategy for different customers or commodities. Sellers can achieve the
goal of dynamic pricing by integrating customer databases that meet specic standards of target
customers [7,8]. When the quantity demanded is random and price sensitive, dynamic pricing
becomes an effective method to maximize prots [9,10]. Varying, dynamic prices are an important
feature of e-commerce pricing. Effectively formulating dynamic pricing strategies is an important
factor for enterprises to succeed in the eld of e-commerce [11,12]. E-commerce companies need
to adopt four methods of dynamic pricing decision-making strategies, namely, a time-based pricing
strategy, a market segmentation and limited rationing strategy, a dynamic marketing strategy, and
comprehensive application based on dynamic pricing [13,14]. The time-based pricing strategy is
implemented according to the price difference that consumers can bear at different times. The key
is to grasp the psychological difference of customers’ price tolerance at different times [15,16].
The basic principles of the market segmentation and limited rationing strategy are as follows:
using different channels, different times, and different energy expenditures, customers have different
price tolerance psychologies; companies have developed special product and service portfolios;
and companies differentiate pricing based on different product congurations, channels, customer
types, and times [17,18]. The dynamic marketing strategy takes advantage of the powerful advan-
tages of the Internet to quickly and frequently implement price adjustments based on changes
in supply and inventory levels to provide customers with different products, various promotional
offers, multiple delivery methods, and differentiated products. In addition, in the actual application
process, the enterprise may consider implementing a certain strategy individually or combining
strategies. When formulating pricing strategies, the best approach is to experiment with specic
customer groups, select the best pricing model [19,20], and then adjust the model accordingly.
In dynamic pricing, companies can use some modeling methods, such as inventory models, data-
driven models, game models, machine learning models, and simulation models, to assist analysis
and decision-making [21,22]. Data-driven models use statistical or simulation techniques to effec-
tively use customer data to calculate appropriate dynamic prices. Currently, dynamic pricing is
CMES, 2021, vol.127, no.1 293
also one of the important research areas of customer relationship management and data mining
technology [23,24]. Negotiation is a dynamic interactive process for the parties involved in the
transaction to reach a transaction agreement. During the negotiation process, all parties to the
negotiation exchanged proposals for negotiation reecting their beliefs and intentions. In each
round of negotiation, the agent proposes negotiation proposals based on its own negotiation
strategy and evaluates the received proposals to determine whether to accept the other party’s
proposal [25,26]. The negotiation process is usually a dynamic process of learning and updating
your beliefs.
Therefore, in-depth study of the application of deep reinforcement learning methods in the
eld of dynamic pricing is of great signicance to the development of articial intelligence, deep
reinforcement learning methods and their applications in dynamic pricing and other elds. We will
review two aspects of deep reinforcement learning technology and its specic application in the
eld of dynamic pricing. First, based on the existing dynamic pricing, the relevant key technolo-
gies of deep reinforcement learning are introduced. Then, the application of deep reinforcement
learning in dynamic pricing is reviewed from different perspectives, and the advantages and
disadvantages are analyzed. Next, we systematically review platform pricing theory and differential
pricing theory, use game theory as the main research method to establish a competitive platform
enterprise pricing game model, and analyze network externalities and consumer switching costs
in mature and emerging markets as well as the impact of enterprise pricing strategies on market
equilibrium to systematically analyze the dynamic pricing behavior of platform companies. The
rst section of this paper is the introduction, the second part introduces the construction of
the e-commerce dynamic pricing model based on data mining, the third section studies the deep
reinforcement learning transaction recognition model, and the fourth section studies the research
on the e-commerce dynamic pricing model. The results and discussion are given in the fth
section, and the sixth section is a summary.
2 Construction of the E-Commerce Dynamic Pricing Model Based on Data Mining
At present, data mining should focus on customer relationship management in the application
research of e-commerce tools. Although some scholars have also proposed the theory of applying
data mining technology to e-commerce dynamic pricing tools, many of theories are scattered and
general. Theoretical analysis, without comprehensive and systematic application analysis, lacks
the overall grasp of the application of data mining in the dynamic pricing of e-commerce, and
the effectiveness of data mining cannot be fully utilized. To this end, this article establishes a
dynamic pricing model for e-commerce based on data mining and proposes applying data mining
technology to dynamic pricing decisions, which will be of great help to e-commerce companies
in pricing decisions. The model is composed of three layers, namely, the data layer, the analysis
layer and the decision layer, from top to bottom [27]. These three levels are closely connected,
and each level contains the application of related theories and technologies of data mining and
dynamic pricing, which together achieve the goal of e-commerce dynamic pricing decisions. The
model is shown in Fig. 1.
2.1 Data Layer
The task of the data layer is to collect data related to pricing decisions and preprocess these
data to form a data warehouse to prepare for the next stage of data mining.
After the data source is selected, the data must be collected in a timely and high-quality
manner and imported into a series of data les, usually in the form of database storage. This step
294 CMES, 2021, vol.127, no.1
can be used to generate and obtain data in the form of network-free action, but it also requires
enterprises to build a basic database in vain and update it in time according to inventory, market
and sales reports. The data collected through various channels may have considerable redundancy,
or there may be inaccurate, incomplete, and inconsistent data. This requires preprocessing the
data if the data are extracted, veried, and cleaned. Conversion, integration and other processes
to improve data quality, form a data collection suitable for data mining, and load it into the
data warehouse.
Perception layer
Online transaction
data
Customer
transaction
information
Dynamic pricing strategy
database
Preliminary dynamic
pricing strategy
Database
Dynamic
pricing model
Deep
reinforcement
learning
E-commerce
platform
Figure 1: E-commerce dynamic pricing model based on deep reinforcement learning
2.2 Analysis Layer
The main tasks of the analysis layer are to use data mining models and related algorithms to
analyze and process the data obtained, to mine knowledge useful for dynamic pricing decisions,
and to form the initial knowledge base. The realization of this stage is the core of the whole model
construction. In dynamic pricing-assisted decision-making tools, methods such as association rules,
classication, clustering, and sequence pattern Analysis can be used.
Correlation analysis aims to mine the data relationships or rules hidden in the data (ware-
house) database, that is, to discover the laws or knowledge of dependence or association between
an event and other events. In e-commerce dynamic pricing tools, association analysis can be
used to nd customer’s views on various product visits and purchases on a website, to determine
various associations of customer buying behavior and to acquire information on customer buying
behaviors and product prices and other product information The relationship between these types
CMES, 2021, vol.127, no.1 295
of information can be used to further discover the relationship between demand and price, which
is an important point for dynamic pricing decisions. The collected basic customer data and
transaction data can use the Apriori algorithm to discover the details of the customers’ purchase
associations [28,29].
2.3 Decision-Making Layer
The decision layer is a key part of the realization of the entire model. The main task of this
layer is to make dynamic pricing decisions based on the knowledge base that established by the
analysis layer and combined with the business strategy of the enterprise.
Through the application of analysis layer data mining technology, one can obtain the char-
acteristics of the access patterns, purchase patterns, habits and preferences of different customer
groups; the correlation characteristics between price and demand and the sales of goods, as well
as the number of people related to the goods and the amount of sales; the predicted value of time
series data of inventory data; etc. Using this basic knowledge, the seller can make preliminary
dynamic pricing decisions. In the time-based strategy, rst determine the appropriate initial is
determined, and factors such as historical sales data, cost information are comprehensively con-
sidered; then, given the initial maximum or minimum price, a double price change basis can be
used to adjust the price by setting a time threshold on the quantity of goods or demand, and then
controlling the time and range of the price changes [30]. When using market segmentation strate-
gies to differentiate pricing based on customer information, the strategies must be understood by
customers, and strategic consumers must adopt appropriate and targeted dynamic pricing strategies
based on their purchase records and price sensitivity [31], thereby achieving customer satisfaction.
The ultimate goal of dynamic pricing for e-commerce companies is to maximize customer
satisfaction or maximize corporate prots; moreover, companies have different goals in differ-
ent periods of their operations and different requirements for pricing strategies. Therefore, the
enterprise pricing decision is a multiobjective decision-making process. To this end, we must rst
establish a multiobjective function. Using various mined related information and forecast data, an
appropriate demand function can also be established, and the price can be adjusted according to
customer demand or corporate sales/inventory. When applying this traditional enterprise dynamic
pricing strategy, there are many mature pricing models that can be referenced. For example, the
pricing model based on inventory control uses dynamic programming to achieve dynamic pricing
and the application of other mathematical models.
3 Deep Reinforcement Learning Transaction Recognition Model
The intelligent behavior between a group of autonomous and intelligent agents, and how they
coordinate with each other to take action to achieve a certain goal forms Multi-Agent System
(MAS) behavior. In MAS, the mutual coordination among agents includes the coordination of
knowledge, goals, skills and planning directions. The goal they achieve may be a solution goal or
a set of several solution goals. According to the denition, the multiagent collaborative solution
model is shown in Fig. 2.
The input layer of the network has no calculation nodes, and is only used to obtain external
input signals. The neurons of the hidden layer and the output layer are the calculation nodes. The
basis function is a linear function and the activation function is a hard limit function. Suppose
the MLP has only one hidden layer, and its input is t1,t2,...,tn. In addition, the hidden layer has
m1neurons, and their outputs are h1,h2,...,hn. Finally, the network output is represented by δp.
296 CMES, 2021, vol.127, no.1
Then, the output of the j-th neuron in the hidden layer is:
hj=fn
i=1
ωijti−δi,j=1, 2 ...n(1)
Strategy solution
Surroundings
Learning model
Knowledge base
Collaborative solution set
Coordination Coordination Coordination
Knowledge base
Strategy selection
Coordinator
Figure 2: Multiagent collaborative solution model
When the multilayer perceptron is used to solve practical problems, it must rst solve the
problem of training the connection weight between the input and the hidden layer; however,
because it is difcult to determine the expected output value of the hidden layer output, the
network weight training cannot be achieved. Therefore, people seek other neural network solutions
to solve the linear inseparable problem, and the BP network is such a network.
An e-commerce platform, the platform often needs to analyze and predict the customers’
online shopping behavior. Based on the customer information database, the e-commerce plat-
form completes real-time and targeted predictions of customers’ online shopping behaviors, thus
embodying intelligent predictions of customer behaviors. Therefore, as a complete predictive model
system, we rst need to use methods such as data mining, machine learning, and statistics to
discover knowledge and extract features from the data. Based on this, we build a knowledge base
of customer online shopping behavior as knowledge guidance, storage and representation and
then establish a system from data input to prediction behavior. The main research contents are
as follows:
CMES, 2021, vol.127, no.1 297
(1) Consumer behavior data processing and feature construction
First, the interactive logs are extracted from the E-commerce interactive system to pre-
pare data related to consumer behavior analysis and prediction. Then, data preprocessing,
including data cleaning, lling missing values and removing outliers, is performed to ensure
the uniqueness of the data to achieve consumer behavior prediction and provide a good
basic guarantee.
(2) Construction of consumer behavior characteristics
Based on the original data, the user purchase behavior features are extracted. According
to different classication methods, the features can be divided into original and extended
or static and dynamic, or two or more categories of features can be combined into a new
feature. To obtain a good prediction effect, the data and characteristics largely determine
the upper limit of the model prediction. Therefore, how to construct suitable characteristics
is the key factor to provide a good guarantee for the analysis of user behavior.
(3) Consumer behavior prediction model
The accuracy of the prediction model is the key to ensuring the prediction and analysis
of consumer behavior. Although there are many prediction models at present, they are
far from meeting the accuracy requirements under real conditions. How to use consumer
static or dynamic data analysis to accurately predict consumer behavior is an extremely
critical technology.
(4) Consumer shopping behavior analysis
In the representational learning of data, the goal is to seek better representation methods
and create better models to learn these representation methods from large-scale unlabeled
data. The workow of consumer shopping behavior analysis based on deep learning is
mainly divided into the following four steps.
Step 1: Prepare and process the data set. This step includes collecting user interaction
information, data cleaning, etc.
Step 2: Feature construction is divided into three stages: feature selection, forming the sam-
ple training set and test set, and feature processing. Feature selection is the key to building a
prediction model. It selects feature sets that are extremely important for classication from a
large number of data sets, thereby improving the model’s prediction accuracy and shortening
the running time. The inconsistency of feature dimensions and units which selected for different
dimensions will affect the weight of the assessment features, which in turn affects the model’s
estimated effect. Therefore, feature management is required to perform normalization.
Step 3: Design and train the prediction model. Select the basic model framework such as
the convolutional neural network (CNN)+recurrent neural network (RNN). Then, using the
framework, randomly sample negative samples of the data, adjust the number of network layers,
determine the loss function, and design the learning rate and other hyperparameters. The BP
algorithm back-propagates using stochastic gradient descent (SGD) or the Adam algorithm to
optimize model parameters.
Step 4: Model verication. Untrained data are used to verify the generalization ability of the
model. If the prediction result is not ideal, you need to redesign the model and conduct a new
round of training. There are several mature deep learning models to date, including deep neural
networks (DNNs), convolutional neural networks (CNNs), deep condence networks (DBNs),
and recurrent neural networks (RNNs). These methods have been used in machine vision,
298 CMES, 2021, vol.127, no.1
natural language processing, bioinformatics, speech recognition and other elds and have achieved
remarkable results.
4 Research on E-Commerce Dynamic Pricing Model
4.1 Deep Reinforcement Learning
The working principle of deep reinforcement learning is similar to that of human learning.
If an action of the agent obtains a positive reward from the environment, then the agent’s future
actions will be enhanced; conversely, if a negative reward is received, then the future actions will
be weakened. The goal of deep reinforcement learning is to learn an action strategy, so that
the system can obtain the largest cumulative reward. In deep reinforcement learning, the agent
selects and executes an action a in the environment, the environment changes to s after accepting
the action, and feeds back a reward signal r to the agent, and the agent selects the subsequent
action according to the reward signal. In research related to dynamic pricing, the goal of deep
reinforcement learning systems is to enable manufacturers to maximize their overall returns while
ignoring the short-term benets of a single transaction. A deep reinforcement learning architecture
generally includes four elements: Strategy, reward and punishment feedback, the value function,
and the environmental model. The environment-related factors of dynamic pricing are numerous
and complex. Previous studies of dynamic pricing in deep reinforcement learning were mainly
based on the following environmental frameworks.
Deep reinforcement learning can be divided into value-based deep reinforcement learning and
policy-based deep reinforcement learning. In deep reinforcement learning based on value functions,
commonly used learning algorithms include the Q-learning algorithm, SARSA algorithm and
Monte Carlo algorithm. In dynamic pricing research based on deep reinforcement learning, these
three algorithms are also frequently used algorithms. (1) Q-learning algorithm. The Q-learning
algorithm is a model-free algorithm, and its iteration equation is expressed as:
δi=mi+1+λmax Q(si+1,α)−Qt(st−αt)(2)
where Q(st+1,α)is the state action value at time t, m is the reward value, λis the discount factor,
a is the learning rate, stis the time difference error, and ais the action that state st+1can perform.
4.2 SARSA Algorithm
SARSA is a strategy algorithm that can nd the optimal strategy through iteration of the
state action value function when the reward function and state transition probability are unknown.
When the state action pair is accessed innitely, the algorithm will converge to the optimal strategy
and state action value function with a probability of 1. The SARSA algorithm adopts relatively
safe actions in learning, so the convergence speed of the algorithm is slow. The iteration equation
is expressed as:
Q(s,α)=Q(s,α)+αλ+λQst,αy (3)
4.3 Monte Carlo Algorithm
The Monte Carlo algorithm does not require complete knowledge of the environment, and
only requires experience to solve the optimal strategy. These experiences can be obtained online
or according to some simulation mechanism. The Monte Carlo method keeps a count of the
frequency of state actions and future rewards and establishes their values based on estimates. The
Monte Carlo technique estimates the return of the average sample based on the sample. For each
state, keep all the states obtained from state, and the value of one state is their average value.
CMES, 2021, vol.127, no.1 299
Especially for periodic tasks, Monte Carlo technology is very useful, especially for periodic tasks.
Since sampling depends on the current strategy, the strategy only evaluates the reward of the
proposed action. The value function update rule is expressed as:
V(st)=V(st+1)+α(λi−V(st)) (4)
where λiis the reward value at time t, and a is the step parameter.
4.4 E-Commerce Dynamic Pricing Model
Dynamic pricing in e-commerce is one of the fastest growing areas in Internet applications.
By applying an online auction-style dynamic pricing model, companies can products based on the
true market value of commodities. In most real markets, only the buyer himself knows exactly
how many items he will be willing to buy at a specic price level. The seller does not have perfect
knowledge of the market demand and cannot accurately understand the buyer’s valuation. The
seller only has statistical information about the market demand. This chapter mainly starts from
the “individual valuation” model and discusses the “online auction” where a single seller provides
auction items, multiple buyers bid on the auction items, and an auction-type dynamic pricing
model exists.
4.4.1 Auction Dynamic Pricing Model and Analysis Based on Uncertain Demand
Suppose that the system is a market environment where a certain auctioneer on the Internet
auctions many items, there are many demanders, and the quantity of demand is uncertain. Let the
set of n demand-side agents sets be N, and let F be the set of all possible allocation combinations
among them. Each distribution combination α∈F,Agent,j∈Nis assigned a monetary amount
vj(α),andvj(α)is private information, that is, an “independent individual valuation”. Indepen-
dence means that each buyer’s personal information is independent of other bidders’ personal
information. Personal valuation means that once a buyer uses his own information to evaluate the
value of the auction target, this valuation will not be subject to his follow-up knowledge of the
impact of any other purchaser’s personal information.
V(N)=Max
α∈F
vj(α)
V(N\j)=Max
α∈F\j
vj(α)(5)
If the auction process is closed, the auction process is as follows: Agents submit their
monetary amount function, and we temporarily assume that they are faithfully submitting their
monetary function. Later, it will be explained that false reporting cannot improve the income of
any agents. The auctioneer chooses the best distribution plan for all calculations of V(N) and V
(N/j). In this way, the agent’s payment is:
V(N\j)=
i=j
viα∗(6)
The net income is:
vjα∗−⎢
⎢
⎢
⎣V(N\j)−
i=j
viα∗⎥
⎥
⎥
⎦(7)
300 CMES, 2021, vol.127, no.1
Suppose that the seller agent S has 5 indivisible commodities and that 5 bidders A1,A2...A5
participate in the bidding. The possible demand and bid of each bidder A1,A2...A5are shown
in Fig. 3, and the revenue of the seller’s agent S is shown in Fig. 4.
Figure 3: Possible demand and bids of bidders
Figure 4: Income of the seller’s agent S
4.4.2 Exchange-Based Dynamic Pricing Model Based on Uncertain Demand
In the online auction MDA market environment, it is assumed that there are m buyers and
n sellers. The number of buyers and the number of sellers are arbitrary, and it is not assumed
that there are more buyers than sellers or more sellers than buyers. Each buyer i =1, 2 ...m wants
to buy Xiunits of homogenous goods. Each seller j =1, 2 ...nhasYjunits of homogenous goods
for sale. To simplify the analysis, it is assumed that Xiand Yjare public information for all
CMES, 2021, vol.127, no.1 301
participants, that is, the m buyers and n sellers know each other’s quantity demanded for the
commodity or quantity supplied of the commodity. However, the reserved price biof buyer i
and the reserved price of seller j are sjis private information, that is, each is an “independent
individual valuation.” The agent in the model assumes that their reserve price is static and remains
unchanged during the auction.
When the auction is over (that is, market liquidation), assume that buyer i purchases tij units
of goods from seller j, and mij is the transaction price of the transaction. In this way, the utility
obtained by buyer i after the auction can be dened as:
ubi=
N
j=1bi−tijmij (8)
The utility obtained by seller j can be dened as:
usj=
N
i=1tij −sjmij (9)
If all information is public, the maximized total market value, that is, the aggregate util-
ity of all agents participating in the auction, can be obtained through the following linear
programming problem:
Max
m
i=1
n
j=1
mij bi−sj(10)
5 Results and Discussion
Since the third-party brushing platform uses exchange information for brushing customers
and merchants as a prot method, to obtain false transaction information, the author entered
the third-party brushing platform by pretending to be a brushing identity and released the billing
information through the third-party platform. Then, the author collected comments and transac-
tion records of fake trading products. In addition, to collect data on normal trading commodities,
the author chose ofcial agship stores (such as Hailan House, ONLY, VERO, MODA, Uniqlo
and other ofcial Tmall agship stores with a high reputation in reality) and combined these
product reviews and transaction records are used as training sets for regular trading products.
Based on this, the author collected the data of nearly 130,000 reviews data and the transaction
record data of the most recent month of the product as the input data set of the recognition
model. After normalizing the data, an independent sample t-test was performed, and the results
are shown in Tab. 1.
It is not difcult to see the convergence of the algorithm in Fig. 5. In the case that the speci-
ed number of iterations is 80, the scale of the problems involved in our discussion can converge
well to the optimal value. As the scale of the problem continues to increase, the maximum number
of iterations can be adjusted according to the specic situation.
Taking the dynamic bidding market as an example, in K transaction cycles, there are N trans-
action agents bidding on M brand cars, and the matching agent calculates and matches the
bids based on the matching transaction model and algorithm. Trading agents are risk-neutral,
and all participate in bidding in a random optimal way. According to the microstructure and
302 CMES, 2021, vol.127, no.1
dynamic trading mechanism, the market equilibrium easily forms for the same type of commodity
bidding; however, when multiple types of goods are matched at the same time, the market status
will become very complicated. Therefore, we designed market price dynamic uctuations and
equilibrium experiments for single commodities and multiple types of commodities.
Table 1: Summary statistics of the characteristics
Is it false N Mean Standard
deviation
Standard error
of the mean
Store registration time 0
Store registration time 1
125
85
0.479
−0.865
0.587
0.927
0.0534
0.1023
Refund dispute rate 0
Refund dispute rate 1
116
78
−0.4789
0.8675
0.0167
1.234
0.00145
0.1356
Product review rate 0
Product review rate 1
118
78
−0.2689
0.4456
0.0428
1.267
0.00256
0.1543
Single product review
Ratio 0
Single product review
Ratio 1
119
77
−0.578
1.036
0.0278
1.0387
0.00267
0.1156
Collection rate 0
Collection rate 1
116
75
−0.234
0.367
0.3768
1.467
0.03345
0.1678
Repeat review rate 0
Repeat comment rate 1
115
82
−0.3567
0.6754
0.265
1.675
0.0243
0.1864
Average comment
Length 0
Average comment
Length 1
122
82
−0.4876
0.7894
0.421
1.234
0.0375
0.1365
Figure 5: Variation curve of individual target value for 80 iterations under 80 periods
In experiment 1, set N =26, K =2, and M =1; and a total of 120 bids were made.
In experiment 2, set N =26, K =36, and M =5; and a total of 140 bids were made; Matching
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and matching will be performed according to the bid price, and the matching transaction price will
be calculated. In addition, based on the actual transaction data of 400 groups of an automobile
trading market, the standard deviation of the matching transaction price is calculated.
Let EquTe represent the degree of equilibrium of market prices. Then, according to the
trading entropy and Walrasian equilibrium, EquTe can be dened as the probability of the
occurrence of an equilibrium trading price.
EquTe =1
K
K
k=1
p∗
k−Ek(Vk)
Ek(Vk)(11)
Here, Ek(Vk)is the expectation of the nal value of the commodity in trading cycle k, p∗
kis
the equilibrium price of market liquidation, K is the number of rounds of the trading cycle,
Time represents the trading cycle, and Price Diff represents the current market transaction price
correction. The experimental results are shown in Figs. 6 and 7.
Figure 6: Price uctuation and equilibrium of a single commodity in 2 rounds of bid-
ding transactions
Fig. 6 shows that in the trading cycle, when the price correction value of the commod-
ity market bidding is at a medium level, the probability that the transaction price reaches an
equilibrium is greater. In fact, the market price dispersion measure is close to the ideal value.
After multiple trading cycles (L =2), the peak sequence of price uctuations forms a Walrasian
equilibrium curve that matches the trading market. Fig. 7 shows that when there are multiple
types of commodities (M =80) participating in multiple rounds of bidding transactions (L =80),
the equilibrium point sequence of various types of commodity transactions forms multiple peaks,
which better reects the market competition and is balanced. The above experimental results show
that in the multiagent matching trading model, the price correction 8 is insensitive to individual
trading agents, and the entire market has good sensitivity to 8. Through multiagent bidding that
continuously adjusts the transaction price, the market can achieve an equilibrium with better
efciency. The experimental results show that the matching transaction model is cost effective
and has good market efciency. According to the market prediction model, the transaction price
uctuation trend of a certain brand car in the auto trading market is predicted. There are
304 CMES, 2021, vol.127, no.1
27 risk-neutral agents participating on the bidding of a certain type of car (using the buyer’s
market as an example). Then the actual transaction price, average bid price and transaction
forecast price uctuation trend of the 7 matching transaction cycles are shown in Fig. 8.
Figure 7: Price uctuation and equilibrium of commodities in 80 rounds of bidding transactions
Figure 8: Fluctuation curve of actual transaction price, average bid price and transaction predicted
price
Fig. 8 shows that the predicted price uctuations is basically between the actual price level
and the average bidding level, which better reects the price trend of this type of car brand in the
market matching transaction. Through market price prediction, the trading agent further adjusts
the bidding strategy to form their initial trading price and belief.
CMES, 2021, vol.127, no.1 305
6 Conclusion
The development of Internet technology and the popularization of the networks have
expanded the application range of data mining, and the application of data mining in e-commerce
tools has become increasingly extensive. This article uses data mining theory and methods and
dynamic pricing-related strategies to establish an e-commerce dynamic pricing model based on
data mining. Based on the mechanism of the model, the auction mechanism is analyzed and
discussed and suggestions for improving pricing strategies are proposed. The comprehensive data
mining of the model system in the application of e-commerce dynamic pricing tools has a rela-
tively general applicability to e-commerce enterprises, which can help enterprises improve customer
satisfaction and economic efciency. The E-commerce platform integrates the production and
sales of the enterprise, and the production and sales are mutually restricted. In the study of the
specic substitution effect of the multiproduct dynamic pricing research, we simply considered the
production constraints, but did not closely integrate production planning and sales and combine
them together. How to adjust commodity prices according to changes in production plans is a
question that requires further study.
Funding Statement: His work is supported by Scientic research planning project of Jilin Provincial
Department of education in 2020: Analysis of the impact of industrial upgrading on employment
of college students in Jilin Province (No. JJKH20200505JY).
Conicts of Interest: The authors declare that they have no conicts of interest to report regarding
the present study.
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