Improving the recommender algorithms with the detected communities in bipartite networks

Zhang, Peng; Wang, Duo; Xiao, Jinghua

doi:10.1016/j.physa.2016.11.076

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…en, we use A w as the input to the method of Riolo et al [11]. As shown in Figure 9(a), we can estimate the correct number k w � 2 of Southern women communities which matches the one determined in [4,21,22]. Figure 9(b) shows that, for the events, k e � 3 receives the most weight but k e � 2 comes a close second.…”

Section: Empirical Networkmentioning

confidence: 77%

“…Finally, we use the code, downloaded from http://www.danlarremore. com/bipartiteSBM/makeEasyCaseNetworks.m, to generate mixed synthetic networks of an easy case for testing with the is network was commonly used as a benchmark for bipartite network community detection algorithms [4,13,21,22], much like the Zachary "karate club" that was used for benchmarking unipartite community detection algorithms. A common way to represent and study bipartite networks consists of projecting them onto links of one kind of vertex [23].…”

Section: An Easy Casementioning

confidence: 99%

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New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

Wang

Liu

2019

Scientific Programming

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Community detection is an important task in network analysis, in which we aim to find a network partitioning that groups together vertices with similar community-level connectivity patterns. Bipartite networks are a common type of network in which there are two types of vertices, and only vertices of different types can be connected. While there are a range of powerful and flexible methods for dividing a bipartite network into a specified number of communities, it is an open question how to determine exactly how many communities one should use, and estimating the numbers of pure-type communities in a bipartite network has not been completed. In our paper, we propose a method named as “biCNEQ” (bipartite network communities number estimation based on quality of filtering coefficient), which ensures that communities are all pure type, for estimating the number of communities in a bipartite network. This paper makes the following contributions: (1) we show how a unipartite weighted network, which we call similarity network, can be projected from a bipartite network using a measure of correlation; (2) we reveal the relation between the similarity correlation and community’s edges in the vertices of a unipartite network; (3) we design a measure of the filtering quality named QFC (quality of filtering coefficient) to filter the similarity network and construct a binary network, which we call approximation network; and (4) the number of communities in each type of unipartite networks is estimated using Riolo’s method with the approximation network as input. Finally, the proposed biCNEQ is demonstrated by both synthetic bipartite networks and a real-world network, and the results show that it can determine the correct number of communities and perform better than two classical one-mode projection methods.

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Section: Empirical Networkmentioning

confidence: 77%

Section: An Easy Casementioning

confidence: 99%

New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

Wang

Liu

2019

Scientific Programming

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“…In the process of seizing the international market, how to choose logistics for cross-border e-commerce export should be considered first: cost, destination, speed, convenience, and risk, especially cost. At the same time, different enterprises should choose the appropriate logistics mode according to the enterprise size, product characteristics, consumer groups, business model, and other factors and analyze various logistics solutions [16]. At present, international postal parcel is the logistics mode with the most complete coverage network.…”

Section: Cross-border E-commerce Logistics Model Analysis and Index S...mentioning

confidence: 99%

Selection Method of Cross‐Border e‐Commerce Export Logistics Mode Based on Collaborative Filtering Algorithm

Ying

2022

Journal of Mathematics

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The combination of the Internet and foreign trade has promoted the rapid development of cross-border e-commerce, and cross-border e-commerce logistics has risen due to the development of cross-border e-commerce. However, the costs incurred by enterprises in the operation of intermediate logistics links have remained high for many years. Considering the cross-border electricity export logistics mode selection, the author of this paper tackles the main existing problems, using literature study, field survey method, case analysis and the collaborative filtering method of major consumer goods export logistics mode, and the advantages and disadvantages in the enterprise’ internal and external environmental analysis and evaluation, and puts forward a scientific logistics coordination mechanism and new export logistics mode. Firstly, the development background of cross-border e-commerce logistics is sorted out, and the research content, research method, and research framework are proposed based on the theoretical research of relevant scholars. Secondly, several logistics modes of cross-border e-commerce export are summarized, including international postal parcel, international express, international special line, etc., and the indicator system of logistics mode selection is determined through comparative analysis of application scope and advantages and disadvantages. Secondly, a logistics selection model based on collaborative filtering algorithm is constructed, and the model is verified by a practical case of a specific export cross-border e-commerce company. Finally, the research conclusions and application strategies of this paper are proposed, so as to provide the most appropriate logistics mode and guarantee suggestions for cross-border e-commerce enterprises and provide ideas and methods for cross-border e-commerce enterprises to choose international logistics mode for different types of import and export goods. The significance of this study was aimed at the current cross-border electricity development mainly by restricting factors, planning the most suitable for the development of China’s cross-border electronic business logistics mode, to some extent, and it has enriched the cross-border electricity business related research in the field of logistics, to inspire transformation of foreign trade enterprises, so as to better promote the cross-border electricity business healthy and orderly development and export logistics mode optimization selection.

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“…Generally, recommender systems can generate a list of recommendations by these approaches: content-based filtering, collaborative filtering, hybrid recommender systems and, so on [4]. Based on the existing research, the conventional CF (collaborative filtering) approaches, which only use user-item rating information to make recommendations, are in two major categories: the memory-based CF and modelbased CF both of which can be used to make recommendations in tagging systems [5].…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Recommendation System Enhanced by Community Detection

Shokrzadeh,

Feizi-Derakhshi,

Balafar

et al. 2023

Scientific Programming

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Many researchers have used tag information to improve the performance of recommendation techniques in recommender systems. Examining the tags of users will help to get their interests and leads to more accuracy in the recommendations. Since user-defined tags are chosen freely and without any restrictions, problems arise in determining their exact meaning and the similarity of tags. However, using thesaurus and ontologies to find the meaning of tags is not very efficient due to their free definition by users and the use of different languages in many data sets. Therefore, this article uses mathematical and statistical methods to determine lexical similarity and co-occurrence tags solution to assign semantic similarity. On the other hand, due to the change of users’ interests over time this article has considered the time of tag assignments in co-occurrence tags for determining the similarity of tags. Then the graph is created based on similarity of tags. For modeling the interests of the users, the communities of tags are determined by using community detection methods. So, recommendations based on the communities of tags and similarity between resources are done. The performance of the proposed method has been evaluated using two criteria of precision and recall through evaluations on two public datasets. The evaluation results show that the precision and recall of the proposed method have significantly improved, compared to the other methods. According to the experimental results, the criteria of recall and precision have been improved, on average by 5% and 7%, respectively.

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Improving the recommender algorithms with the detected communities in bipartite networks

Cited by 9 publications

References 29 publications

New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

Selection Method of Cross‐Border e‐Commerce Export Logistics Mode Based on Collaborative Filtering Algorithm

Graph-Based Recommendation System Enhanced by Community Detection

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