Role of Artificial Intelligence in Online Shopping and its Impact on Consumer purchasing behaviour and Decision

Jangra, Gaurav; Jangra, Monika

doi:10.1109/iccsea54677.2022.9936374

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…because artificial intelligence will analyze and adjust goods/services in e-commerce to able adjust to customer needs or give the recommended products. In other words, artificial intelligence will help customers choose and in the final decision for buy recommended products [7,18,19,21,24,28,29].…”

Section: Resultsmentioning

confidence: 99%

“…E-Commerce is always associated with activities in buying and selling products and services on platforms that only use the internet. The application of artificial intelligence here will help E-Commerce in various aspects such as product searching to success in buying goods [3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…There have been many studies and research that discuss artificial intelligence in influencing product recommendations [14], the AI can influencing customer behavior in determining the items they want to buy [7]. and use in the world can improve marketing because it can increase sales and can connect from one business to another [2,15,16].…”

Section: Introductionmentioning

confidence: 99%

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Digital Business Transformation: Analysis of the Effect Artificial Intelligence in E-Commerce’s Product Recommendation

Louis,

Noerlina,

Syahchari

2024

A.I.S.

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The purpose of this study is to determine whether artificial intelligence used in E-Commerce influences product recommendations for users. This study explains how much influence artificial intelligence on product recommendations supplied by E-commerce in terms of consumer behavior in making purchasing decisions. Research methods. This research used bibliometric analysis to find the mapping of this topic with articles period 2017 to 2023 from Scopus database. Of the 103 articles were showed by keyword and analyzing the articles according to the relate of the content about 29 articles were finally obtained. The research result is Artificial Intelligence has influence for E-commerce, recommendation system, decision support system, customer behaviour’s, and customer trust. Product recommendations have an impact on E-Commerce. Conclusion. However, from the literature review, founded that there are still a few journals discussing related to considerations to the implementation regarding the use of AI in e-commerce "Consumer behaviour", "Customer Trust", "Purchasing decisions". This study is also useful to generate additional AI-related research in e-commerce and unquestionably for a fresh subject will be covered especially in context of product recommendations on E-commerce.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Digital Business Transformation: Analysis of the Effect Artificial Intelligence in E-Commerce’s Product Recommendation

Louis,

Noerlina,

Syahchari

2024

A.I.S.

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“…Recommendation basis on Preferences: User Preference Collection: Begin by collecting user preferences during the onboarding process or through a dedicated preferences section in the app. Gather information such as preferred shop types (e.g., clothing, electronics, restaurants), cuisine types (e.g., Italian, Chinese, vegetarian), and price range preferences [5][6][7][8]. Data Segmentation: Organize the shop and cuisine data into relevant segments based on the user preferences collected.…”

Section: Implementation Initializationmentioning

confidence: 99%

A Survey of On Personalized E-Commerce

Ankit Jha,

Sankalp Kinikar,

Sudip Ghevade

et al. 2024

Int Res J Adv Engg Mgt

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The Personalized e-commerce application proposed by us incorporates several essential modules to provide a seamless and convenient shopping experience for users. The app utilizes GPS or network-based location services to determine the user's location and search for nearby shops within a specified radius of 5 km. The app also allows users to set preferences such as cuisine, product category, and price range, which are used to filter the list of nearby shops and display only those that match the user's preferences. The app provides relevant information for each shop, such as its name, address, product offerings, prices, and rating options to get directions to the selected shop. Additionally, the app will enable users to make purchases either by redirecting them to the shop's website or by implementing an in-app purchasing process. With these features, the proposed personalized e-commerce app offers a personalized and convenient shopping experience for users, allowing them to find and purchase products from nearby shops with ease.

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“…Association Rule mining (ARM) has widely applied in many fields for data mining applications such as business (2) , healthcare service (3) , and website analysis (4) . To provide better rule mining, the mass amount of database needs to be analysed, so the cloud can give enough support for making strong computing and power to the ARM applications (5) .…”

Section: Introductionmentioning

confidence: 99%

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

Mangayarkkarasi

2024

IJST

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Background/Objectives: It is becoming more common for data owners to outsource data mining tasks and storage to cloud service providers as a result of the rising costs of maintaining IT infrastructures for large-scale data mining. This trend, however, also raises security concerns about unauthorized breaches of data confidentiality and outcome integrity. Methods: This research considers this scenario in which cloud user can encrypt their data and store it to cloud environment. In order to perform mining operation, the user needs to outsource the task to cloud servers. Then, the cloud server performs the mining task on the encrypted data and share the encrypted association rule to the cloud user. Yet, existing single cloud server systems have privacy leakage issues since their work focuses on either database privacy or item privacy. To remedy this gap in the literature, this study maintains both database privacy and item privacy during the frequent itemset mining process. For item privacy, it first describes a universal safe multiplication protocol with a single cloud server. We build the inner product rules, comparison rules, frequent itemset protocol, and final association rule mining process that is secure against privacy leaking on top of this multiplication protocol. During this Association Rule Mining (ARM) operation, it provides two level of protection to data privacy. This model is designed with distributed Elgamal cryptosystem and sub-protocols for item and database privacy along with Deep learning-based Support Vector Machine (SVM) for secure rule generation. Findings: The proposed method is named as two-level privacy preserving method association with Deep SVM model (2-level:D-SVM), provides guaranteed solutions to the confidentiality of the outsourced cloud data and minimizes the user interaction during association rule mining task. Here, data on breast cancer and heart disease are used, and the effectiveness of the proposed model is demonstrated by comparison to existing models. According to the study, at 25000 transactions, the proposed 2-level:D-SVM model stands for 52% and 50% more efficient than Parallel Processing (PP) and Privacy-preserving Collaborative Model Learning (PCML) techniques in terms of computing cost. Additionally, the proposed model performs 34%, 22%, 6%, and 4% better in terms of execution time https://www.indjst.org/

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Role of Artificial Intelligence in Online Shopping and its Impact on Consumer purchasing behaviour and Decision

Cited by 6 publications

References 8 publications

Digital Business Transformation: Analysis of the Effect Artificial Intelligence in E-Commerce’s Product Recommendation

Digital Business Transformation: Analysis of the Effect Artificial Intelligence in E-Commerce’s Product Recommendation

A Survey of On Personalized E-Commerce

Two Level Privacy Preserving Model for Association Rule Mining in Cloud Using Deep-SVM Method

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