中国科学院重庆绿色智能技术研究院机构知识库

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	Popularity and Novelty Dynamics in Evolving Networks
	Abbas, Khushnood1,2,3; Shang, Mingsheng2; Abbasi, Alireza3; Luo, Xin2; Xu, Jian Jun2; Zhang, Yu-Xia4
	2018-04-20
摘要	Network science plays a big role in the representation of real-world phenomena such as user-item bipartite networks presented in e-commerce or social media platforms. It provides researchers with tools and techniques to solve complex real-world problems. Identifying and predicting future popularity and importance of items in e-commerce or social media platform is a challenging task. Some items gain popularity repeatedly over time while some become popular and novel only once. This work aims to identify the key-factors: popularity and novelty. To do so, we consider two types of novelty predictions: items appearing in the popular ranking list for the first time; and items which were not in the popular list in the past time window, but might have been popular before the recent past time window. In order to identify the popular items, a careful consideration of macro-level analysis is needed. In this work we propose a model, which exploits item level information over a span of time to rank the importance of the item. We considered ageing or decay effect along with the recent link-gain of the items. We test our proposed model on four various real-world datasets using four information retrieval based metrics.
DOI	10.1038/s41598-018-24456-2
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	8页码:10
收录类别	SCI
WOS记录号	WOS:000430537800009
语种	英语