Inferring users' preferences through leveraging their social relationships

Deng, Xiaofang; Wu, Leilei; Ren, Xiao-Long; Jia, Chun-Xiao; Zhong, Yuansheng; Lü, Linyuan

doi:10.1109/iecon.2017.8217011

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…They indicated that tags can effectively build up relations between existing objects and new ones, thereby providing solid recommendations of new objects. Deng et al [9] introduced the Social Mass Diffusion (SMD) method based on a mass diffusion process in the combined network of users' social network and user-item bipartite network. They showed that the SMD can generate more personalized recommendations for new users than the global ranking based on popularity.…”

Section: Cold-start Problemsmentioning

confidence: 99%

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Zhang

Lü

2020

Information Sciences

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Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and time-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34, 469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies' domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors' domain preference.Our main goal is to fill this gap by designing and validating recommendation system techniques to identify suitable investors for new startups. To this end, we analyze 34,469 investment events collected from www.itjuzi.com [1]. As we focus on startups that received no previous investments in the past, widely-studied recommendation techniques based on bipartite networks [49] are not applicable here since the new startups with no previous investors turn out to be isolated nodes in the investor-company bipartite network, making them unreachable by diffusion processes. Our problem can be classified as a cold-start problem [35]: When a new actor enters the system, there is insufficient past information to provide him/her with a recommendation [26].To overcome the cold-start problem, we resort to tagging techniques [16].On the www.itjuzi.com platform, each startup is required to provide several tags that define its business scope. We found that the investors have a strong preference toward a small number of tags -in particular, in the majority of cases, an investor tends to invest in startups that feature her favorite tag. Hence, motivated by this finding and the key role played by the industry field in investment decision making [10, 28], we use tags as a key piece of information to generate recommendations.By leveraging startups' tag information, we construct three different tripartite network representations of the investment system. The most natural tripartite representation is one where investors are connected to the startups they invested in, and startups are connected to their self-reported tags -we refer to this representation as the tagcompany-investor (TCI) representation. This representation is natural because it is directly based on the collected ...

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Section: Cold-start Problemsmentioning

confidence: 99%

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Zhang

Lü

2020

Information Sciences

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“…These algorithms will further improve the efficiency of information-filtering systems. In addition, multiple explorations [31,32] on information filtering considering external constraints have also been made.…”

Section: Introductionmentioning

confidence: 99%

Structural centrality of networks can improve the diffusion-based recommendation algorithm

Kong

Y²,

Zhang³

et al. 2022

Front. Phys.

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The recommendation system has become an indispensable information technology in the real world. The recommendation system based on the diffusion model has been widely used because of its simplicity, scalability, interpretability, and many other advantages. However, the traditional diffusion-based recommendation model only uses the nearest neighbor information, which limits its efficiency and performance. Therefore, in this article, we introduce the centralities of complex networks into the diffusion-based recommendation system and test its performance. The results show that the overall performance of heat conduction algorithm can be improved by 184%–280%, using the centrality of complex networks, reaching almost the same accuracy level as the mass diffusion algorithm. Therefore, the recommendation system combining the high-order network structure information is a potentially promising research direction in the future.

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Analyzing of Personalized Recommendation Model of Social Network Users Based on Big Data

Xiao-mei

2020

2019 6th International Conference on Dependable Systems and Their Applications (DSA)

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Inferring users' preferences through leveraging their social relationships

Cited by 3 publications

References 28 publications

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Recommending investors for new startups by integrating network diffusion and investors’ domain preference

Structural centrality of networks can improve the diffusion-based recommendation algorithm

Analyzing of Personalized Recommendation Model of Social Network Users Based on Big Data

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