Guohui Song^1,2, Yongbin Wang^1,*, Jianfei Li¹, Hongbin Hu¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1621-1641, 2023, DOI:10.32604/cmc.2023.040095

Abstract Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity. The… More >

Navdeep Bohra^1,2, Vishal Bhatnagar³, Amit Choudhary⁴, Savita Ahlawat², Dinesh Sheoran², Ashish Kumari^2,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 205-221, 2023, DOI:10.32604/iasc.2023.030708

Abstract The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user’s interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will… More >

Ramsha Saeed¹, Saddaf Rubab¹, Sara Asif¹, Malik M. Khan¹, Saeed Murtaza¹, Seifedine Kadry², Yunyoung Nam^3,*, Muhammad Attique Khan^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.2, pp. 533-547, 2021, DOI:10.32604/cmes.2021.014355

Abstract The substantial competition among the news industries puts editors under the pressure of posting news articles which are likely to gain more user attention. Anticipating the popularity of news articles can help the editorial teams in making decisions about posting a news article. Article similarity extracted from the articles posted within a small period of time is found to be a useful feature in existing popularity prediction approaches. This work proposes a new approach to estimate the popularity of news articles by adding semantics in the article similarity based approach of popularity estimation. A semantically enriched model is proposed which… More >

Weijin Jiang^{1, 2, 3, 4}, Fang Ye^{1, 2, *}, Wei Liu^{2, 3}, Xiaoliang Liu^{1, 2}, Guo Liang⁵, Yuhui Xu^{2, 3}, Lina Tan^{1, 2}

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2263-2275, 2020, DOI:10.32604/cmc.2020.010082

Abstract With the rapid development of information technology, the explosive growth of data information has become a common challenge and opportunity. Social network services represented by WeChat, Weibo and Twitter, drive a large amount of information due to the continuous spread, evolution and emergence of users through these platforms. The dynamic modeling, analysis, and network information prediction, has very important research and application value, and plays a very important role in the discovery of popular events, personalized information recommendation, and early warning of bad information. For these reasons, this paper proposes an adaptive prediction algorithm for network information transmission. A popularity… More >

Jianji Ren¹, Shan Zhao¹, Junding Sun¹, Ding Li², Song Wang³, Zongpu Jia¹

Computer Systems Science and Engineering, Vol.33, No.4, pp. 259-265, 2018, DOI:10.32604/csse.2018.33.259

Abstract In the Content-Centric Networking (CCN) architecture, popular content can be cached in some intermediate network devices while being delivered, and the following requests for the cached content can be efficiently handled by the caches. Thus, how to design in-network caching is important for reducing both the traffic load and the delivery delay. In this paper, we propose a caching framework of Prefix-based Popularity Prediction (PPP) for efficient caching in CCN. PPP assigns a lifetime (in a cache) to the prefix of a name (of each cached object) based on its access history (or popularity), which is represented as a Prefix-Tree… More >

Meiju Yu¹, Ru Li^{1, *}, Yuwen Chen²

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 321-336, 2020, DOI:10.32604/cmc.2020.010831

Abstract Named Data Networking (NDN) is one of the most excellent future Internet architectures and every router in NDN has the capacity of caching contents passing by. It greatly reduces network traffic and improves the speed of content distribution and retrieval. In order to make full use of the limited caching space in routers, it is an urgent challenge to make an efficient cache replacement policy. However, the existing cache replacement policies only consider very few factors that affect the cache performance. In this paper, we present a cache replacement policy based on multi-factors for NDN (CRPM), in which the content… More >

Yong Yu¹, Yongjun Luo¹, Tong Li², Shudong Li^{3, *}, Xiaobo Wu⁴, Jinzhuo Liu^{1, *}, Yu Jiang^{3, *}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 489-507, 2020, DOI:10.32604/cmc.2020.07616

Abstract Personalized recommendation algorithms, which are effective means to solve information overload, are popular topics in current research. In this paper, a recommender system combining popularity and novelty (RSCPN) based on one-mode projection of weighted bipartite network is proposed. The edge between a user and item is weighted with the item’s rating, and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users. RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in… More >

Kemal Akyol^1,*, Baha Şen²

CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 69-80, 2019, DOI:10.32604/cmc.2019.08143

Abstract The popularity of news, which conveys newsworthy events which occur during day to people, is substantially important for the spectator or audience. People interact with news website and share news links or their opinions. This study uses supervised learning based machine learning techniques in order to predict news popularity in social media sources. These techniques consist of basically two phrases: a) the training data is sent as input to the classifier algorithm, b) the performance of pre-learned algorithm is tested on the testing data. And so, a knowledge discovery from the data is performed. In this context, firstly, twelve datasets… More >

Kai Dong^1,2,*, Taolin Guo¹, Xiaolin Fang¹, Zhen Ling¹, Haibo Ye³

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 197-213, 2019, DOI:10.32604/cmc.2019.03903

Abstract Sina Weibo, an online social network site, has gained popularity but lost it in recent years. Now we are still curious on the number of posts in Sina Weibo in its golden age. Besides checking this number in Sina’s operating results, we aim to estimate and verify this number through measurement by using statistical techniques. Existing approaches on measurement always rely on the supported streaming application programming interface (API) which provides proportional sampling. However no such API is available for Sina Weibo. Instead, Sina provides a public timeline API which provides non-proportional sampling but always returns a (nearly) fixed number… More >