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.

中文翻译：

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不断发展的网络中的流行度和新颖性动态。

网络科学在现实世界现象的表示中起着重要作用，例如在电子商务或社交媒体平台中呈现的用户项双向网络。它为研究人员提供了解决复杂现实问题的工具和技术。识别和预测商品在电子商务或社交媒体平台中的未来流行度和重要性是一项艰巨的任务。有些作品会随着时间的流逝而反复流行，而有些则只有一次流行和新颖。这项工作旨在确定关键因素：受欢迎程度和新颖性。为此，我们考虑了两种新颖性预测：第一次出现在热门排名列表中的商品；以及在过去的时间窗口中不在热门列表中但在最近的过去时间窗口之前已受欢迎的项目。为了确定热门商品，需要仔细考虑宏观分析。在这项工作中，我们提出了一个模型，该模型可以在一段时间内利用项目级别的信息来对项目的重要性进行排名。我们考虑了老化或衰变效应以及这些项目的最近链接增益。我们使用四个基于信息检索的指标在四个不同的现实世界数据集上测试了我们提出的模型。