Development of conceptual model to increase customer interest using recommendation system in e-commerce

Wijaya, I Wayan Rizky; Mudjahidin,

doi:10.1016/j.procs.2021.12.194

Cited by 20 publications

(3 citation statements)

References 28 publications

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“…The development of the retail industry has a vital impact on people's livelihood, the economy, and society. At the time of building the main body of the domestic big cycle, doing a good job in the transformation and optimization of the retail industry and injecting new vitality into the economic development will greatly enhance the domestic market potential and improve the efficiency of the domestic large-scale circulation [4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Consumer Recommendation Behavior and Market Equilibrium in E-Commerce from the Perspective of Social Media

Jiang

Chen

2023

Advances in Multimedia

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Under the strategic goal of building a domestic economic cycle, the development quality of social e-commerce in my country needs to be improved urgently. The main reason for the low quality of social e-commerce development in my country is that the natural advantages of social e-commerce, such as high-efficiency dissemination of information and increased consumer utility due to improper sales model design, cannot play a role. However, the lack of research on social e-commerce systems makes it difficult for retailers to obtain effective theoretical guidance for their sales model design. Based on this, this paper takes the three most important and important social e-commerce sales models in my country at present—recommended reward model, online group purchase model, and online crowdfunding model as the research objects, and systematically analyzes the consumer recommendation behaviors in the three sales models. By analyzing and market equilibrium, while improving the theoretical research of social e-commerce, it also provides theoretical basis and policy guidance for the optimal mode design of social e-commerce under different sales modes. This paper considers the consumer recommendation behavior in social networks and analyzes the main characteristics of the three sales models in view of the development status and specific problems of social e-commerce in my country, and then constructs retailer-existing consumer-potential The three-agent dynamic Stackelberg game model of consumers analyzes the influence mechanism and market equilibrium of consumer recommendation behavior under three sales models.

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Section: Introductionmentioning

confidence: 99%

Analysis of Consumer Recommendation Behavior and Market Equilibrium in E-Commerce from the Perspective of Social Media

Jiang

Chen

2023

Advances in Multimedia

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“…This sophisticated approach analyzes diverse factors such as past purchases, browsing history, and demographic information to create a highly personalized shopping experience. By providing unique recommendations aligned with each customer's specific interests and needs, businesses aim to enhance customer engagement and satisfaction [17]. Whether suggesting similar products based on past purchases or offering personalized promotions, this strategy not only contributes to a more enjoyable and efficient shopping experience but also fosters customer loyalty through a thoughtful and individualized approach [9].…”

Section: Personalized Product Recommendation Strategiesmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

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“…Governments, enterprises, and other organizations rely on the collection and analysis of big data to provide reliable support for behavior prediction [1], customer segmentation [2], interest recommendation [3], and so on. However, while providing users with more accurate and reliable services through data mining, the leakage of users' personal information has also become an important issue [4].…”

Section: Introductionmentioning

confidence: 99%

Improved clustering algorithm for personal privacy and security protection of elderly consumers

Jiang

2023

Int. J. Simul. Multidisci. Des. Optim.

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With the advancement of technology, there is an increasing emphasis on the personal privacy and security of elderly consumers. This article focuses on the personal privacy and security protection of elderly consumers. Based on the K-means (KM) clustering algorithm, the optimal value was obtained using the monarch butterfly optimization (MBO) algorithm. The migration operator and adjustment operator of the MBO algorithm were enhanced using differential variation algorithm and adaptive methods to obtain a modified monarch butterfly optimization (MMBO) algorithm. Then, to ensure secure protection during clustering, differential privacy (DP) was employed to add noise perturbation to data to obtained a method called DPMMBO-KM algorithm. In experiments on the UCI dataset, it was found that the MMBO-KM algorithm had better clustering performance. Taking the Iris dataset as an example, the MMBO-KM algorithm achieved the highest accuracy of 93.21%. In the application to recommendation systems, the DPMMBO-KM algorithm achieved higher F1 values under different privacy budgets; the average was 0.06. The results demonstrate that the improved clustering algorithm designed in this article can improve clustering results while ensuring personal privacy and data security, and also perform well in recommendation systems.

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Development of conceptual model to increase customer interest using recommendation system in e-commerce

Cited by 20 publications

References 28 publications

Analysis of Consumer Recommendation Behavior and Market Equilibrium in E-Commerce from the Perspective of Social Media

Analysis of Consumer Recommendation Behavior and Market Equilibrium in E-Commerce from the Perspective of Social Media

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Improved clustering algorithm for personal privacy and security protection of elderly consumers

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