Recommendation networks of homogeneous products on an E-commerce platform: Measurement and competition effects

Zhu, Zhen; Wang, Shan; Wang, Fei; Tu, Zihang

doi:10.1016/j.eswa.2022.117128

Cited by 18 publications

(6 citation statements)

References 60 publications

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“…Kolotylo‐Kulkarni et al (2021) aimed to define information disclosure in e‐commerce and proposed a complete theoretical model and a series of propositions regarding consumer information disclosure in e‐commerce. Zhu et al (2022) studied the recommendation network of homogeneous products on an e‐commerce platform, considering the competitive effect of the product recommendation network. Currently, most e‐commerce research is relatively macro level.…”

Section: Literature Reviewmentioning

confidence: 99%

Referral strategy of e‐commerce platform under different selling modes

et al. 2023

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This study considers a market comprising two suppliers and one e‐commerce platform. The two competitive suppliers sell products to consumers through the platform. The platform can choose three selling modes: reseller, market, and mixed. Additionally, we consider two referral strategies—namely, exclusive and nonexclusive recommendations—and build a three‐party Stackelberg game model. Per the findings, the difference in the players' decision‐making results exhibits an essential relationship with the recommendation market's initial size. A counterintuitive result is that the product price is not affected by the commission rate in the market mode. This provides referential insights for platforms and suppliers.

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Section: Literature Reviewmentioning

confidence: 99%

Referral strategy of e‐commerce platform under different selling modes

et al. 2023

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“…Literature [9] proposed a data classification method based on lightGBM algorithm and applied it to the E-commerce advertisement recommendation problem to achieve personalized advertisement recommendation based on E-commerce platform; Literature [10] proposed an E-commerce platform design method based on fuzzy clustering algorithm, which clustered the E-commerce platform users to provide an auxiliary method for the E-commerce platform users; Literature [11] used the Apriori association rule algorithm to classify E-commerce platform users and provide accurate customer service for E-commerce platform users; Literature [12] uses K-means, PCA and integrated learning algorithms to construct a personalized tourism product recommendation method, and adopts user behavioral data to validate and analyze the proposed method; Literature [13] adopts a deep learning method to identify tourism products analysis, and combined with the evaluation degree of tourism products to give appropriate tourism products; literature [14] constructs a tourism E-commerce platform by improving the K-means clustering algorithm to achieve personalized customization of tourism products. According to the analysis of the above research, although the integration of machine learning algorithms, data mining technology and tourism E-commerce has achieved certain results, there are still some limitations by then [15]:…”

Section: Introductionmentioning

confidence: 99%

Design Method for Travel E-commerce Platform Based on HHO imparoved K-means Clustering Algorithm

Dang,

Yang

2024

ICST Transactions on Scalable Information Systems

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Convenient and intelligent tourism product recommendation method, as the key technology of tourism E-commerce platform design, not only provides academic value to the research of tourism E-commerce platform, but also improves the efficiency of personalized recommendation of tourism products. In order to improve the quality of tourism recommendation, this paper proposes a tourism E-commerce platform design method based on HHO improved K-means clustering algorithm. Firstly, the Harris optimization algorithm is used to improve the K-means algorithm to construct a user-oriented tourism product recommendation strategy; then, combined with the XGBoost algorithm, an item-oriented tourism product recommendation strategy is proposed; secondly, the two strategies are mixed to construct a personalized tourism product recommendation model. Finally, the effectiveness of the proposed method is verified by simulation experiment analysis. The results show that the recommendation accuracy of the tourism E-commerce platform design method proposed in this paper reaches more than 90%, and the recommendation response time meets the real-time requirements, which can provide personalized tourism product recommendation for platform users and enhance the purchase of tourism products.

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“…Recommender systems are widely used to assist users in quickly finding interesting information from vast data sets. Currently, recommender systems have broad applications in various domains such as session-based recommendation [1,2], product recommendation [3], news recommendation [4], music recommendation [5], and social recommendation [6].…”

Section: Introductionmentioning

confidence: 99%

A Recommendation Method Based on Multi-Source Heterogeneous Hypergraphs and Contrastive Learning

Wan,

Ding

2024

IEEE Access

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Fusion of multi-source information is one of the primary methods to alleviate data sparsity in recommender systems. Hypergraphs have shown remarkable capabilities in dealing with the diversity of multi-source information, especially in modeling high-order user-item relationships. However, most hypergraph research focuses on constructing hypergraphs using a particular type of hyperedge, which might not fully capture the high-order implicit associations among heterogeneous information sources. Furthermore, existing Hypergraph Attention Networks (HyperGAT), mainly emphasizes on information propagation between nodes and hyperedges, insufficiently exploring density information within complex hyperedges. Moreover, when hypergraphs combine data from multiple heterogeneous graphs, redundant information from different viewpoints can reduce the effectiveness of multivariate modeling hyperedges. Thus, we propose a recommendation algorithm based on multi-source heterogeneous hypergraphs and contrastive learning (MHCLR), which improves recommendation accuracy through multi-source information fusion and higherorder information correlation. First, multi-source heterogeneous hypergraphs (MHC) are generated by combining distance, behavior, attribute, and prediction hyperedges. We mine associations among multisource information, enhancing high-order semantic connections among users and items. Then, a Spatial Density Hypergraph Attention Network (SD-HGAT) is proposed based on composite hyperedges, which enriches the user and item embedding representations by focusing on nodes and hyperedges density. Finally, we design a multiple cross view comparative learning (MCC) that compares views centered around knowledge graphs with hypergraphs, improving the accuracy of multivariate relationship modeling and enabling multi-level user profiling construction. It is observed that MHCLR outperforms the baselines in terms of recall, precision, and NDCG based on the experimental results by Yelp, Last-FM, and Douban.

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Recommendation networks of homogeneous products on an E-commerce platform: Measurement and competition effects

Cited by 18 publications

References 60 publications

Referral strategy of e‐commerce platform under different selling modes

Referral strategy of e‐commerce platform under different selling modes

Design Method for Travel E-commerce Platform Based on HHO imparoved K-means Clustering Algorithm

A Recommendation Method Based on Multi-Source Heterogeneous Hypergraphs and Contrastive Learning

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