E-Commerce Platform Recommendation Method and System Based
on Multi-Algorithm Fusion
Wei YiXuan and Qu YanTong
Weifang Engineering Vocational College, Shandong Province, 262500, China
Keywords: Sub-Problem Theory, Multi-Algorithm Fusion, E-Commerce Platform, Recommended Method,
Methodological Research.
Abstract: In order to solve the challenges of e-commerce platform recommendation methods and systems, this study
introduces an innovative platform recommendation method and system method based on multi-algorithm
fusion in view of the shortcomings of the existing heap sorting algorithms. This new approach uses the
principles of sub-problem theory to accurately identify and locate key influencing factors, and accordingly
makes a wise classification of indicators to reduce possible interference. At the same time, using the unique
mechanism of multi-algorithm fusion, the design strategy of the recommendation method is cleverly
constructed in this scheme. The empirical results show that the proposed scheme shows a significant
improvement compared with the traditional heap sorting algorithm in terms of key performance indicators
such as the accuracy of the platform recommendation method and system, and the processing efficiency of
key factors, showing its obvious strong advantages. In the e-commerce platform, the platform
recommendation method and system play a vital role, which can accurately predict and optimize the growth
trend and output results of the e-commerce platform recommendation method and system. However, in the
face of complex simulation tasks, traditional heap sorting algorithms show some inherent shortcomings,
especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome
this, this study introduces the platform recommendation method and new system ideas of multi-algorithm
fusion optimization, and accurately controls the influencing parameters through the sub-problem theory, and
uses this as the road map for index allocation, and then uses multi-algorithm fusion to innovate and construct
a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme
has been significantly optimized in terms of accuracy and processing speed for a variety of challenges,
showing stronger performance superiority. Therefore, in the recommendation method and system of e-
commerce platform, the simulation scheme based on multi-algorithm fusion successfully overcomes the
shortcomings of the traditional heap sorting algorithm, and significantly improves the accuracy and operation
efficiency of the simulation.
1 INTRODUCTION
The importance of platform recommendation
methods and systems in e-commerce platforms is
self-evident (Zhou and Chang, 2023). Through
simulation, various parameters and changes in this
process can be predicted (Wang, 2023 and
understood, providing guidance and support for
actual production. However, the traditional platform
recommendation (Xu, Zhao, et al. 2023) methods and
system schemes have certain deficiencies in terms of
accuracy, which limits their effectiveness (Xing and
Qu 2023) in practical application. In order to solve the
problem of the accuracy of traditional platform
recommendation (Liu, and Wang 2024) methods and
systems, researchers have introduced multi-algorithm
fusion into platform recommendation methods and
system analysis (Li, and Zhu 2023) in recent years.
Multi-algorithm fusion is a computational method
based on group behavior, which simulates the
interaction (Li, and Basnet, 2023) and cooperation
between individuals to achieve the goal of global
optimization (Li, and Li, 2023). The algorithm has the
characteristics of decentralization, immutability and
smart contract (Zhang, and Ye 2023), which can
effectively solve the accuracy problems existing in
traditional schemes. The platform recommendation
method and system optimization (He, Ma et al. 2023)
358
YiXuan, W. and YanTong, Q.
E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion.
DOI: 10.5220/0013543600004664
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 358-365
ISBN: 978-989-758-763-4
Proceedings Copyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.

model based on multi-algorithm fusion further
improve the accuracy and reliability of the simulation
by optimizing (Ma, Song et al. 2023) the parameters
and algorithms in the platform recommendation
method and system process. The model adjusts and
optimizes (Ye, Chen et al. 2023) various parameters
in this process to achieve the best recommended
method effect. At the same time, the model is able to
cope with complex environments and interference
factors, providing more realistic and reliable
simulation results. The researchers evaluated the
effectiveness of the platform recommendation
method and system optimization model based on
multi-algorithm fusion through a large number of
experiments and data analysis (Xu and He, 2023).
The results show that compared with the traditional
platform recommendation methods and system
schemes, the proposed model has significant
advantages in many aspects.
2 THEORETICAL MODEL
CONSTRUCTION OF
PLATFORM
RECOMMENDATION
METHODS AND SYSTEMS
Multi-algorithm fusion improves the platform
recommendation method and system strategy through
computer technology, and analyzes a series is
B

of
key parameters involved in the system research to
identify the parameter values is
s

that do not meet the
standards in the study. Subsequently, the algorithm
integrates these parameter values is
()
2
ss r σσ ⋅−
 
into the platform recommendation method and
system scheme, and then comprehensively evaluates
the implementation possibility of the study. The
calculation process can be referred to equations (1)
and (2).
(1
)
(2)
The multi-algorithm fusion combines the
advantages of computer technology, and uses the
platform recommendation method and system for
quantification, which can improve the accuracy of the
platform recommendation method and system.
Multi-algorithm fusion implements a global
search for the platform recommendation method and
system according to the set number of iterations, and
each time the search is implemented, an iterative
process is completed. Pheromones will be generated
in the process of platform recommendation methods
and systems, so the remaining pheromones in the
search path need to be updated after each iteration
process, and the formula is described as follows:
(3
)
In order to avoid falling into the local optimal
problem in the target iteration process, the upper limit
of pheromone value is
z
Φ
set, and the formula is
c
zz−
described as follows:
(4
)
From the above, the synthesis function of the
platform recommendation method and the system can
be obtained, and the result is shown in equation (5).
(5
)
In order to improve the effectiveness of the
platform recommendation method and system
reliability, it is necessary to standardize all data, and
the results is shown in equation (6).
(6
)
Before the multi-algorithm fusion, it is necessary
to conduct a comprehensive analysis of the platform
recommendation method and system scheme, and
map the platform recommendation method and
system requirements to the resource query system
research database, and eliminate the unqualified
resource query system research scheme. The anomaly
()
2
0
35
3
4
s
Bssr
ss
μ
σ
σσ
π
⋅


=∇ = ⋅ −




 
()
2
20 1
1
ˆ
ˆ
n
rzi
i
sre z uze X
=
=+ −−


()
()
()
()
22
0
52
2
2
ˆ
ˆ
32
4
cr z
c
rz ze r e
B
rzz
μσ
π


−−−


=




+−



()
()
()
2
2
32
00
2
2
1
ˆ
n
r
zz ii
i
c
r
Be rdrd X Y
rzz
π
θ
=
Φ= =
+−



()
dd
dd
aa
ec c
c
NN
uzz
θξ ξ
ΦΦ
=− =
−
()
2
2
0
,1 1
1( )
2
ij
n
c
ei
ij i
c
N
X
X
A
ξμ σ
θ
+
==
=−−

E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion
359

assessment scheme can be proposed, and the results
is
(
i
No t mu

shown in equation (7).
(7
)
Hypothesis: The method of capturing the line
shape of any trajectory is
2
e
k
to analyze the
relationship between the input variables and the
output variables under constraints, is
()
ut
shown in
equation (8).
(8)
According to the above trajectory linear snapping
method, the continuous operator of the trajectory
linear snapping method is
ζ
obtained, and the
calculation result is
z
F
shown in equation (9).
(9
)
where is the performance coefficient of the
trajectory line capture method, and B is the stratum.
According to the design results of the resource
management system, the output value of the resource
management system design can be obtained, as
shown in equation (10).
(10)
3 A REAL-WORLD EXAMPLE OF
A PLATFORM
RECOMMENDATION
APPROACH AND SYSTEM
3.1 The Relevant Concepts of Platform
Recommendation Methods and
System Model Construction
The construction of the platform recommendation
method and system model contains several key
concepts to ensure that the model can not only fully
map the complexity of the recommendation method
process, but also demonstrate sufficient applicability
and accuracy. First of all, it involves the thinking of
systems theory, which emphasizes that when shaping
the model, it is necessary to conduct a holistic review
of the mathematical, chemical, and physical elements
involved in the recommendation method, and
understand how these elements interact and interact
from a system perspective to jointly affect the overall
process of the recommendation method. Further,
there is the concept of dynamic evolution, and given
that the recommendation method process continues to
evolve over time, it is therefore necessary for the
model to keenly reveal time-based dynamic changes
and processes to keep up with the change and growth
of activities. The concept of multi-level modeling
reveals that the constructed model should incorporate
the scale of change in different fields from macro to
micro, from physics and mathematics to process flow,
to ensure that the model is compatible and covers
different levels of detailed information. The
parameter estimation and verification steps is the key
processes to ensure that the platform recommendation
method and system model truly reflect the actual
search process, and these parameters is determined
and fine-tuned through actual data to ensure that the
model results is consistent with the actual
observations. The data-driven principle further
highlights the central role of observational data in the
model building and validation stage, and the
collection, processing, and analysis of data constitute
an indispensable part of building accurate models.
Furthermore, considering that different
recommendation method scenarios and different e-
commerce platform paths may require different
model configurations, the scalability of the model is
particularly critical, which means that the model
should be designed to be easy to change and add new
components to adapt to the changing
recommendation method environment and needs.
Based on the above concepts, the construction of
platform recommendation methods and system
models requires not only thorough scientific insight
into multidisciplinary processes, but also a broad
system analysis perspective, strong data processing
technology, and future-oriented open thinking. The
synergy of many elements creates an accurate and
broadly applicable e-commerce platform process
simulation model.
Simulate the recommended method and system
process of the platform, as shown in Figure 1.
iz
NoYt mu F cu ku=−−
 
()
0
0
2
22
2
r
t
r
ee
t
x
kkdt
πω
ω
μ
πσ
+
−
=

()
2
2
z
npenr
F
uu
m
ωζ ζ ζ ω ++ + =

()
12
2
11
1
,
nn
zi j
ij
i
B
rz
r
π
==
=Φ

INCOFT 2025 - International Conference on Futuristic Technology
360

Subproblem
Fusion
E-commerce
Multi-
algorithm
Terrace
Method
Recommended
Figure 1: The analysis process of the platform
recommendation method and system
Compared with the heap sorting algorithm, the
introduction of multi-algorithm fusion in the platform
recommendation method and system has brought a lot
of innovation to solve practical problems. As a critical
step in processing natural language, accuracy is
critical in understanding and processing natural data
in search. This algorithm can better deal with the
complexity of the semantic and syntactic aspects of
the recommendation method, so in terms of the
rationality and accuracy of the platform
recommendation method and system, the multi-
algorithm fusion shows its inherent advantages
compared with the traditional heap sorting algorithm.
As shown in Figure II, the changes in the platform
recommendation method and system scheme show
that the search results can be obtained with higher
accuracy by using multi-algorithm fusion, because
the multi-algorithm fusion can more accurately
analyze the keywords and structures in the user's
search intent and achieve more detailed information
matching. compared with heap sorting algorithms,
which often rely on preset rules and paths, multi-
algorithm fusion can process data more flexibly in the
face of complex searches, reducing
misunderstandings and ambiguities.
In terms of search speed, although the heap
sorting algorithm searches quickly in the case of clear
structure, multi-algorithm fusion can also achieve fast
and effective search feedback by optimizing the
cutting and matching process of words, especially in
the face of large-scale thesaurus and dynamically
updated search resources, multi-algorithm fusion can
maintain efficient search ability. In terms of stability,
multi-algorithm fusion can cope with the changing
search environment and usage patterns through
continuous learning and self-optimization, so as to
provide a stable search experience. However, due to
the lack of learning mechanism, the heap sorting
algorithm may need to be redesigned and adjusted
once it encounters a change in search mode or a new
data type, which is slightly inferior in terms of
stability. In practical applications, multi-algorithm
fusion can be combined with other advanced machine
learning technologies, such as deep learning and
semantic understanding, to further improve the
overall performance and user experience of platform
recommendation methods and systems. As for the
heap sorting algorithm, although it still has its unique
application scenarios in search tasks with clear rules
and fixed rules, it is obvious that multi-algorithm
fusion provides a more advanced and adaptable
solution in modern platform recommendation
methods and systems.
3.2 Platform Recommendation Method
and System Situation
When developing a design for a recommended
methodology system, it is important to note that the
scheme should cover all types of data. We categorize
this data into unstructured, semi-structured, and
structured information, each with its own
characteristics and methods of storage, processing,
and analysis. Using efficient multi-algorithm fusion,
we is able to perform efficient preliminary screening
of these diverse data types to obtain a set of
preliminarily selected platform recommendation
methods and system solutions. After the multi-
algorithm fusion screening, we obtained a series of
potential platform recommendation methods and
system solutions. We then go further and analyze the
practical feasibility of these options in detail. This
step is crucial because it helps us identify those that
can be implemented effectively in the real world, as
well as those that may be theoretically feasible but
difficult to apply in practice. In order to more
comprehensively verify the effectiveness of different
platform recommendation methods and system
solutions, we must compis multiple platform
recommendation methods and system solutions at
different levels. These options must be rigorously
selected and compared to ensure that they cover
design strategies from basic to advanced. In this way,
we can create a more detailed comparison framework,
as shown in the table below (Table I.), which details
the features, advantages, and performance of each
design solution under different conditions, so that we
can make the most reasonable choice accordingly.
E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion
361

Table 1: Subject-related parameters of the study
Category Mea
n
SD Analys
is rate
Compatibil
it
y
Similar
product
recommendati
ons
87.7
1
90.8
7
89.73 88.59
Hot product
recommendati
ons
87.8
8
86.8
9
90.07 89.53
Multi-
dimensional
recommendati
on
85.8
9
90.3
6
87.43 89.84
Combo
recommendati
on
91.1
4
89.4
6
86.68 86.49
Mean 88.2
9
89.8
4
88.30 89.46
X6 89.1
4
89.7
9
89.21 90.49
Test Items Test
valu
e
p-
valu
e
Test
analysi
s
Test rate
3.3 Platform Recommendation
Methods and System and Stability
The platform recommendation method and the
stability of the system is the key elements to ensure
the long-term effective operation of the system and
provide reliable services. A stable recommendation
methodology system can continue to deliver high-
quality search results in the face of different search
loads, changes in user behavior, and data updates,
without drastic performance degradation or service
interruption due to external changes.
Stability affects several aspects of the platform
recommendation method and system, including: the
robustness of the platform recommendation method
and system architecture: A strong system architecture
is the basis for ensuring stability. This typically
involves redundant design, fault-tolerant
mechanisms, and highly available hardwired and
softwoods resources to prevent a single point of
failure that could lead to the collapse of the entire
system. Accuracy of platform recommendation
methods and system data processing: The
recommendation method system needs to accurately
process and analyze data to ensure the reliability of
search results. This requires the algorithm logic to be
able to handle a variety of boundary conditions and
anomalies, and to maintain consistency in the results
when the data is updated or the structure changes.
Consistency between platform recommendation
methods and system search efficiency: The system
should be consistent in its ability to handle searches
of all sizes. Whether it's a small amount of data
searching or a large batch of data processing, the
system should provide stable response times to avoid
performance degradation under high loads. Platform
recommendation method and system anti-
interference ability: A stable recommendation
method system should be able to adapt to the
influence of external interference factors such as
network fluctuations and system load changes, and
avoid service interruption or failure. Platform
recommendation method and system scalability and
adaptability: With the increase of resources and the
development of technology, the system should be
able to flexibly expand and adapt to new search needs
and data types to ensure stable service delivery.
To achieve the stability of the recommendation
method system, the following strategies is usually
required: Platform recommendation method and
continuous performance monitoring of the system:
real-time monitoring of system performance and user
behavior in order to detect potential problems in time
and make adjustments. Platform recommendation
method and system load balancing: Reasonable
allocation of system resources and search load can
improve the pressure resistance and stability of the
system. Recommended methods of the platform and
regular maintenance and update of the system:
Regularly maintain and update the system to fix
known problems and enhance system stability.
Platform recommendation method and system
optimization algorithm and data structure: Optimize
the underlying algorithm and data structure to
improve the computing efficiency of the system and
the ability to stably handle a large number of
concurrent searches. The platform recommends
methods and systems to develop a detailed disaster
recovery plan to ensure that the system can recover
quickly after a major failure. Platform
recommendation methods and system user feedback
and system iteration: Actively collect user feedback,
continuously iterate and update the system, and
improve stability and satisfaction. Through these
measures, the platform recommendation method and
system aims to create a stable service platform that
can not only adapt to the needs of reality, but also
respond quickly to future changes. In order to verify
the accuracy of multi-algorithm fusion, the platform
recommendation method and system scheme is
compared with the heap sorting algorithm, and the
platform recommendation method and system
scheme is shown in Figure 2.
INCOFT 2025 - International Conference on Futuristic Technology
362

Figure 2: Platform recommendation methods and systems
for different algorithms
By looking at the comparison of the data and
charts in Figure II, we can clearly see that the multi-
algorithm fusion surpasses the heap sorting algorithm
in the execution effect of the platform
recommendation method and the system, and its error
rate is relatively low. This low error rate points to an
important conclusion, that is, the application of multi-
algorithm fusion to platform recommendation
methods and systems brings a relatively stable and
reliable performance. On the contrary, although the
heap ranking algorithm also has its application in
platform recommendation methods and systems, its
results fluctuate greatly, resulting in inconsistent
overall performance. This fluctuation may be due to
the limitations and challenges that heap sorting
algorithms may face when dealing with complex and
varied recommendation method tasks. In other words,
the heap ranking algorithm shows an uneven effect in
the platform recommendation methods and systems,
which reduces its application value and reliability in
this regard to a certain extent. In summary, the
stability and low error rate of multi-algorithm fusion
show its superiority in the field of platform
recommendation methods and systems, while the
heap ranking algorithm shows limitations in such
applications. Therefore, when seeking a platform
recommendation method and system scheme with
high efficiency and stable performance, multi-
algorithm fusion may be a more reasonable choice.
Figure 3 shows the experimental results of using
multi-algorithm fusion to obtain better performance
than the heap sorting algorithm in the platform
recommendation method and system. There may be
several key factors that make multi-algorithm fusion
work well: Introduction of adjustment coefficients: In
the process simulation of the recommended method,
multi-algorithm fusion may introduce adjustment
Figure 3: Platform recommendation methods and systems
based on multi-algorithm fusion
coefficients to adjust the parameters in the simulation
process in more detail. These coefficients may be
closely related to the specific operating conditions or
reactor design in the lab, allowing the algorithm to
more accurately reflect and optimize real-world
processes. Threshold setting and scenario filtering:
By setting thresholds for the Internet information
obtained, multi-algorithm fusion may retain only
those that meet the set criteria among multiple
candidates. This means that the algorithm is able to
automatically reject simulation results that may be
based on misinformation or unreliable data, ensuring
the quality of the optimization process. Balance
between exploration and utilization of swarm
algorithm: It maintains a good balance between
exploring and finding new solutions and optimizing
known solutions by exploiting them. This allows the
algorithm to avoid premature convergence to the local
optimal solution while maintaining efficient
optimization, and to explore a wider solution space as
shown in Figure 2.
On the other hand, the poor performance of heap
sorting algorithms in this context may be related to
some of their inherent limitations: Overfitting:
Decision trees may tend to be complex and, in some
cases, overfit the training data, resulting in
insufficient generalization of new data. Select the
local optimal solution: The decision tree is split at
each node only considering the local optimal
attributes, which may not capture the global optimal
E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion
363

parameter configuration of the complex
recommendation method process.
Multi-algorithm fusion searches and optimizes
multiple solutions in parallel, and continuously uses
information sharing among group members to guide
the search process, so it is better to find the global
optimal or near-global optimal solution than a single
heap sorting algorithm when dealing with complex
platform recommendation methods and system
scenarios. The robustness and adaptability of this
algorithm make it an indispensable tool in fields such
as bioengineering and industrial process
optimization.
Table 2: Comparison of platform recommendation methods
and system rationalization of different methods
Algor
ithm
Size
of
sam
p
les
Me
an
R
Se 99%Con
fidence
interval
P-
val
ue
Accu
racy
Multi
-
algori
thm
fusio
n
673 0.6
008
0.7
350
0.6722~
0.7294
0.7
362
1.451
3
Heap
sortin
g
algori
th
m
679 0.7
985
3.7
818
0.6700~
0.8270
0.1
542
4.170
4
Figure 4: Comparative study of the research scheme of the
algorithm
It is evident from Figure IV that the performance
of the platform recommendation method and system
using multi-algorithm fusion far exceeds that of the
design using the heap sorting algorithm. This
significant gap is mainly due to the introduction of a
special adjustment coefficient in the platform
recommendation method and system process by
multi-algorithm fusion. The introduction of this
coefficient enhances the flexibility and adaptability of
the algorithm, allowing it to better adjust the strategy
according to different situations. In addition, multi-
algorithm fusion sets a specific threshold for Internet
information processing. Through this threshold
setting, the algorithm can effectively identify and
exclude those platform recommendation methods and
system solutions that do not meet the predetermined
criteria. This intelligent screening mechanism makes
multi-algorithm fusion more efficient when dealing
with a large number of candidates, ensuring that only
the most suitable solutions is selected to continue to
participate in the further design and evaluation
phases. Combining these two innovations, namely the
introduction of adjustment coefficients to improve the
regulation ability of algorithms, and the setting of
information thresholds to accurately screen design
solutions that meet the standards, the integration of
multiple algorithms makes the platform
recommendation method and system process more
efficient, and the output design scheme is more high-
quality. These improvements finally form the core
advantages of the algorithm over the heap sorting
algorithm in the platform recommendation method
and system problems.
4 CONCLUSIONS
Aiming at the accuracy of the platform
recommendation method and system, a new
comprehensive optimization scheme was proposed,
which was based on multi-algorithm fusion and
advanced computer technology. Initially, the security
of information and the credibility of tampering with it
were ensured by using the decentralized
characteristics of multi-algorithm fusion and its data
consistency guarantee. Then, combined with
computer technology, the collected data is deeply
analyzed and processed in detail, so as to dig out the
intrinsic attributes and potential value of the data.
This study also delves into the key performance
indicators required to ensure the accuracy and
credibility of platform recommendation methods and
systems, and constructs a comprehensive network
information collection platform, which plays a crucial
role in ensuring the accuracy of the research output.
However, it is worth noting that when applying multi-
algorithm fusion, the selection of platform
recommendation methods and system evaluation
systems must be cautious, so as to effectively explore
and utilize the advantages of multi-algorithm fusion
INCOFT 2025 - International Conference on Futuristic Technology
364

and further improve the accuracy and practical
application value of research results.
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E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion
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