A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

Bai, Li; Hu, Mi; Ma, Yunlong; Liu, Min

doi:10.3390/app9153141

Cited by 9 publications

(5 citation statements)

References 41 publications

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“…In the study by Bai et al [72], data sparsity is a major challenge the authors aim to address. According to them, the primary cause of data sparsity arises from users providing reviews only when they are genuinely interested in a product.…”

Section: B Data Sparsitymentioning

confidence: 99%

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Bodduluri,

Palma,

Kurti

et al. 2024

IEEE Access

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This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of singlearchitecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decisionmaking, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.

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Section: B Data Sparsitymentioning

confidence: 99%

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Bodduluri,

Palma,

Kurti

et al. 2024

IEEE Access

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show abstract

“…Pinduoduo reduces redundant intermediaries and, therefore, additional costs. Pinduoduo provides merchants with an information-rich and transparent platform [18]. Merchants have access to the information they need about their users on the platform, such as the number of orders through a grouping campaign.…”

Section: Reduction Of Intermediate Linksmentioning

confidence: 99%

Analysis of Advantages and Disadvantages of China's Online Trade Development - A Case Study of Pinduoduo

Huang¹

2023

BCPBM

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With the continuous development of Internet technology, the e-commerce industry has become more critical in the context of the information age. People are increasingly aware of the comfort and convenience brought by online shopping in their daily lives. From this, various business platforms have also contended, striving to compete for their world in the fierce market. When the e-commerce industry is in full swing, Pinduoduo stands out among many e-commerce platforms as a dark horse, quickly occupying a place in the e-commerce market in a short time. Such success is not accidental. It was after the Pinduoduo team comprehensively inspected the current e-commerce market situation in China and accurately judged various opportunities based on their advantages. This article thoroughly analyzes the basis of Pinduoduo's development, business strategy, the multiple options and challenges it seizes after entering the market, institutional innovation, and model innovation. In addition, taking the other two robust e-commerce platforms, Taobao and JD, as an example, the advantages of traditional e-commerce platforms and bottlenecks in the development period are analyzed. The rise of Pinduoduo has expanded the market size and space of e-commerce, highlighted the development potential of novel business models, and inspired other companies to break through the limitations of their mindset and strive for market-oriented innovation and transformation. In this context, this article takes Pinduoduo as the research object. By analyzing Pinduoduo, a successful case in the Internet enterprise market, we focus on how latecomers can innovate through business models and then come to the top in the fierce market competition. This paper's research conclusions and suggestions have specific practical guiding significance for the future development of e-commerce in China.

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“…The parallel hardware implementation based algorithm is developed for embedded CF applications with large datasets [68]. The online recommendation algorithm is designed, which combines clustering and CF techniques to improve the accuracy of online recommendation systems for group-buying applications [69]. The recommender system development is discussed that uses several algorithms to obtain groupings [70].…”

Section: Features-based Recommendersmentioning

confidence: 99%

“…Nowadays, the new recommender algorithms are required for real-world applications, because of the following reasons [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][21][22][23][24][29][30][31][32][43][44][45][46][47][66][67][68][69][70]:…”

Section: Features-based Recommendersmentioning

confidence: 99%

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

2020

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Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| < 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes <10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.

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A Hybrid Two-Phase Recommendation for Group-Buying E-commerce Applications

Cited by 9 publications

References 41 publications

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Analysis of Advantages and Disadvantages of China's Online Trade Development - A Case Study of Pinduoduo

Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

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