Online Auction System Using AI

Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology
ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR1360
Online Auction System Using AI

Pratham Patil1; Ashwin Patil2; Purva Mhatre3; Sanjay Jadhav4
Mahatma Gandhi Mission’s College of Engineering and Technology, Navi Mumbai, Maharashtra
Abstract:- Net has pushed the globalization which  Bidding time: Admin can set bidding time on posting
addresses the interaction and integration many of people product for selling, the winner is asserted after time elapse.
,one-of-a-kind business institutes, government our bodies,  This utility makes use of Bootstrap as a the front-cease
and lots of more. As people are exposed to unlimited and Python, sq. because the back-end.
variety of quantitative products through use of internet,
they seek for the predicted one at affordable or favorable  Advantages:
fee and time. On-line biding has become distinguished
way to the expectations of on-line customers since it  Excludes noisy crowds like traditional machine in which
excludes the want of physical presence at the bidding users have to sit down and bid.
location and the product can be acquired on the low-  Lets in smooth procedure without dealing with any
priced price. When bidding is performed on a bidding problems of conventional device.
floor this machine will permit users to get right of entry  No perfect time table for bidding so, bidder can bid for
to the bidding the usage oa an internet portal. Images can merchandise at their very own will from everywhere and
also be considered. Online bidders can vicinity bids at any every time.
moment and their bids will be displayed on a display at  The bidding can be made on a global level.
their own window. Authentication may be key for the
online machine and get admission to credentials can be  Disadvantages:
provided simplest to proven users. Users can set
standards for automobile bidding including restriction  The person cannot view the object in character.
amount, next bid increase quantity and many others.
 No human interplay.
Information can be accrued by using the system to choose
 If there are terrible quality pictures then it's of no need.
which objects evoked the maximum interest in bidders.
We purpose to construct a tool that recommends the best
II. EXISTING SYSTEM
acceptable product for person based on consultation.
E-Bay Bidding machine supply examples of the huge
Keywords:- Online, Credentials, Authentication,
increase that has been achieved, especially via internet
Recommends.
technology. E-Bay is the most used online bidding retailer
with over 80% of the online bidding market, brags that, on
I. INTRODUCTION
any given day, there are greater than 12 million items indexed
A web bidding venture is a system that holds online on eBay throughout over 18,000 classes. in the 2nd region of
biddings for various products online and bidders. An Admin 2003, E-Bay reported report internet sales of $509.three
permits users to set up their products for biddings on their million, up 91% from the identical period in 2002. The
behalf and also consumer can sign up and bid for various primary fact of eBay is everyone can sign on and begin
merchandise available for bidding. The device additionally promoting without any revel in. according the pricing model,
sellers who do now not have a store can obtain one hundred
includes products taken care of through categories and via
loose listings consistent with month.
price.
 Online Bidding Project Consists of the Following  Blessings:

Features:
 Excessive great products, widely recognized and reliable
internet site.
 Person Login: consumer can register on line and then get
admission to the system on authentication.  Because the wide variety of customers will increase,
greater overview/guidelines can be furnished.
 Kind merchandise: consumer can kind products by using
category and charge variety.
 Negative Aspects:
 Bidding merchandise: Admin can installation merchandise
for bidding by offering information and minimum bid.
 Earnings Loss
 Delete products: Admin can delete products.
 Scam costs
 Admin Login: Admin can installation products for bidding
via offering information and minimum bid. Admin can  Customer support
login to gadget and view merchandise as well as remarks
or even delete other person’s products.
IJISRT24APR1360 www.ijisrt.com 646

III. LITERATURE SURVEY regulatory frameworks to uphold ethical standards and

user trust. [10]
 Intelligent Bidding Mechanisms for Online Auctions:
 Problem Statement
 Smith, J., Johnson, A. This papers reviews various The main project lies in growing an AI-pushed on-line
intelligent bidding mechanisms employed in online public sale gadget that ensures transparency, security and
auction systems. It discusses AI techniques such as fairness throughout the whole bidding technique. The device
machine learning, game theory, and evolutionary have to be capable of correctly detecting and preventing
algorithms utilized to optimize bidding strategies and fraudulent sports, optimizing bidding techniques for members,
improve auction outcomes. [2] and imparting personalized recommendations primarily based
on person possibilities and historic information. Furthermore,
 Bidding Strategy Optimization: it need to foster an environment that promotes identical
opportunities for all participants, regardless of their
 Wang, H., Li, Q. Wang and Li propose a reinforcement geographical location or economic heritage.
learning approach for developing bidding strategies in
online auctions. The paper explores how agents can learn  Proposed System
optimal bidding behaviors through interactions with the The proposed system will make the bidding process a
auction environment, leading to improved efficiency and whole lot easier for each online and offline users. customers
competitiveness. [4] could be capable of bid on products despite geographical
 R. Chen et al. (2018) conducted a study on the application difficulties. The Proposed gadget may be globally hosted so,
of reinforcement learning algorithms for optimizing anybody can take component in bidding. each bid could be
bidding strategies in online auctions, presenting a novel recorded with a time which in the end helps in advice and
approach that adapts to dynamic market conditions and also preserve digital report. The proposed online auction
user preferences. [1] system aims to revolutionize the current auction landscape by
integrating sophisticated AI technologies to enhance every
 Personalized Recommendation Engines: aspect of the auction process. With a focus on transparency,
security, and user-centric functionality, the system will offer
 K. Zhang et al. (2017) discussed the development of a a comprehensive and seamless auction experience for both
personalized recommendation engine for online auction buyers and sellers. Through the implementation of advanced
platforms, emphasizing the utilization of collaborative AI algorithms, the system will personally recommends
filtering and content-based recommendation techniques to modifications to individual user preferences and historical
enhance user engagement and satisfaction. [6] bidding behaviours , thereby optimizing user engagement and
 Y. Wang et al. (2019) proposed a hybrid recommendation satisfaction. Enhanced fraud detection mechanisms, powered
system that combines deep learning and matrix by AI, will be deployed to effectively identify and prevent
factorization methods to deliver accurate and tailored fraudulent activities such as shill bidding and bid shielding,
product recommendations based on user behaviour and ensuring a secure and trustworthy platform for all
preferences in online auctions. [7] participants. Moreover, the system will prioritize fairness and
inclusivity by implementing AI-driven decision-making
 Data Security and Privacy: models and fairness metrics, promoting equal opportunities
for all users regardless of their background or financial
 T. Li et al. (2018) investigated the implementation of capacity. To guarantee the security and confidentiality of
blockchain technology to ensure data security and privacy sensitive data, the system will integrate robust encryption
in online auction systems, addressing concerns related to techniques and stringent security protocols, safeguarding user
data tampering and unauthorized access. [9] information and transactions from potential breaches. By
 L. Chen et al. (2020) emphasized the significance of combining these features, the proposed system seeks to
secure multi-party computation protocols in preserving redefine the online auction experience, fostering trust,
the privacy of sensitive auction data while facilitating efficiency, and fairness, and ultimately establishing a more
reliable and user-friendly auction ecosystem for all
efficient data analysis and processing. [1]
stakeholders.
 Fairness and Transparency in Online Auctions:
IV. METHODOLOGY
 J. Kim et al. (2019) proposed a fairness-aware auction
Developing an online auction system integrated with AI
mechanism that integrates AI-driven fairness metrics to
involves a comprehensive methodology that encompasses
mitigate biases and ensure equal opportunities for all
various stages of planning, development, implementation, and
participants in online auctions, fostering a more inclusive
evaluation.
and equitable marketplace. [8]
 E. Lee et al. (2021) examined the ethical implications of
AI algorithms in online auction systems, advocating for
the adoption of transparent decision-making models and

 Here is a Structured Methodology for Creating Such a V. RESULT

System:
 Requirement Analysis:
Conduct a thorough analysis of the requirements,
including user expectations, security standards, scalability
needs, and regulatory compliance. Identify key functionalities
such as user authentication, bidding mechanisms, product
catalog management, and payment processing.
 System Design:
Create a detailed system architecture, outlining the
components, modules, and their interactions within the online
auction system. Design the database schema to manage user
profiles, product information, bidding history, and
transactional data.
 AI Integration Planning:
Define the AI components required, such as fraud Fig 1 User Interface
detection algorithms, recommendation engines, and decision-
making models. Select appropriate AI frameworks and
libraries that align with the project goals and technical
requirements.
 Development:
Implement the core functionalities of the online auction
system, including user registration, authentication, and
authorization. Integrate AI components by developing fraud
detection algorithms, personalized recommendation engines,
and transparent decision-making models.
 Testing and Quality Assurance:

Conduct rigorous testing to identify and resolve any
system bugs, performance issues, or security vulnerabilities.
carry out unit trying out, integration testing, and consumer
reputation trying out to ensure the system functions
seamlessly and meets consumer expectancies.
Fig 2 Customer Panel
 Deployment:
Prepare the system for deployment on the chosen servers
or cloud infrastructure. ONLINE AUCTION SYSTEM
USING AI Configure the necessary networking and security
protocols to ensure data protection and system accessibility.
 User Training and Documentation:

Provide comprehensive user training materials and
documentation to facilitate user understanding and adoption
of the online auction system. Offer user support and guidance
to address any queries or challenges encountered during
system usage.
 Evaluation and Iterative Improvement:

Continuously monitor the system performance, user
feedback, and market trends to identify areas for
improvement. Gather data on user engagement, transaction
volumes, and security incidents to inform future
enhancements and updates to the online auction system.
Fig 3 Category Selection Panel

Fig 4 Featured Actions
Fig 7 Chat bot
VI. CONCLUSION
The integration of AI technologies into the online

Fig 5 Latest Actions auction system represents a significant leap forward in the
evolution of digital marketplaces. The implementation of AI-
driven fraud detection algorithms and personalized
recommendation engines has substantially enhanced the
security, transparency, and user experience within the auction
platform by leveraging AI, the system has effectively
minimized fraudulent activities, ensuring a secure and
trustworthy environment for all participants. The personalized
recommendation engines have not only increased user
engagement but have also fostered a deeper sense of trust and
satisfaction among the users. Moreover, the emphasis on
transparency and fairness through AI-driven decision-making
models has promoted inclusivity and equal opportunities,
establishing a more ethical and accountable auction
ecosystem. The scalability and reliability of the system have
been crucial in accommodating the growing user base and
data volume, guaranteeing a seamless and uninterrupted
auction experience. Ultimately, the successful integration of
AI has transformed the online auction system into a more
efficient, secure, and user-friendly platform, setting new
Fig 6 Deposit and Payment Panel standards for digital marketplace landscape. from above all

elaboration here in short we would say online auction system [8]. Kim, J., Lee, S., Park, H., & Choi, Y. (2019). A
will give new approaches and dimensions to the auction fairness-aware auction mechanism for online
system. It will encourage buyers and sellers to participate in marketplaces: Integrating AI-driven fairness metrics.
the auction process. Break free from borders, space Journal of Artificial Intelligence Research, 66, 835-
constraints and time constraints. Finally, online auctions have 857.
emerged as another convenient way to meet the expectations [9]. Li, T., Zhang, H., Chen, X., & Wang, J. (2018).
of online buyers; because it does not require bidders to be Implementing blockchain technology for enhancing
physically present in a competitive location and products can data security and privacy in online auction systems.
be obtained at affordable prices. Buyers can purchase Journal of Computer Security, 26(6), 623-639.
products at their own affordable prices. [10]. Lee, E., Park, S., Choi, H., & Kim, Y. (2021). Ethical
implications of AI algorithms in online auction
FUTURE SCOPE systems: Towards transparent decision-making
models and regulatory frameworks. Ethics and
Looking to the future, the online auction system Information Technology, 23(1), 87-104.
integrated with AI holds immense potential for further growth [11]. https://blog.statsbot.co/recommendation-
and advancement. Future developments may focus on refining systemalgorithms-ba67f39ac9a3.
fraud detection mechanisms, integrating advanced natural https://www.scribd.com/document/335759638/Compa
language processing for more intuitive user interactions, and rati ve-Analysis-of-Ecommerce-Websites-a-Case-
leveraging predictive analytics for optimized bidding Study
strategies. Additionally, there is room for enhancing [12]. Xiling Cui, Vincent S. Lai and Connie K.W. Liu
recommendation engines for more personalized and accurate “Consumer Behaviour in Online Auctions: A
product suggestions. The integration of virtual and augmented Review”, Electronic Markets Vol. 18 No.4.
reality technologies may also offer users immersive and [13]. Janhavi Baikerikar, Vaishali Kavthekar, Esmond
interactive auction experiences. With these advancements, the Dsouza, Steffie Fernandes, Mureil Dsouza, “Hammer
AI-integrated online auction system is poised to set new Down-An Online Auction Application”, IEEE, 2017.
standards for efficiency, security, and users satisfaction in the [14]. International Journal of Research Publication and
digital marketplace. Reviews, Vol 3, no 4, pp 2103-2105, April 2022.
[15]. Zhang Jie, Zhang Yaping, “Research on Duration and
REFERNCES Bid Arrivals in eBay online Auctions in the Internet”,
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Modeling for Bid Optimization in Online Bidding
Systems." Information Sciences, 450-451, 321-336.
[2]. Smith, J., & Johnson, A. (2019). "Intelligent Bidding
Mechanisms for Online Auctions." Journal of
Artificial Intelligence Research, 45, 123-145.
[3]. Tong Zhao, Julian McAuley, Mengya Li, Irwin King,
Improving Recommendation Accuracy using
networks of Substitutable and Complementary
Products.IEEE,2017
[4]. Wang, H., & Li, Q. (2020). "Reinforcement Learning-
Based Bidding Strategy in Online Auctions." Expert
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[5]. SaiWu,WeichaoRen,ChengchaoYu,GangChen,Dongxi
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[6]. Zhang, K., Zhang, L., Wang, L., & Du, H. (2017).
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hybrid recommendation system combining deep
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Online Auction System Using AI

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Volume 9, Issue 4, April – 2024 International Journal of Innovative Science and Research Technology

ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/IJISRT24APR1360

Online Auction System Using AI

 Online Bidding Project Consists of the Following  Blessings:

IJISRT24APR1360 www.ijisrt.com 646

III. LITERATURE SURVEY regulatory frameworks to uphold ethical standards and

IJISRT24APR1360 www.ijisrt.com 647

 Here is a Structured Methodology for Creating Such a V. RESULT

 Testing and Quality Assurance:

 User Training and Documentation:

 Evaluation and Iterative Improvement:

IJISRT24APR1360 www.ijisrt.com 648

Fig 4 Featured Actions

Fig 7 Chat bot

The integration of AI technologies into the online

IJISRT24APR1360 www.ijisrt.com 649

IJISRT24APR1360 www.ijisrt.com 650

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