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4th International Conference of Reliable Information and Communication Technology (IRICT)

作者： Rouane, Oussama Belhadef, Hacene Bouakkaz, Mustapha Univ Constantine 2 Abdelhamid Mehri Constantine Algeria Univ Amar Telidgi Comp Sci Dept Fac Sci Laghouat Algeria

ISBN: (纸本)9783030335823;9783030335816

In this paper, we have proposed a novel word embedding-based biomedical text summarizer. Biomedical words are represented by real dense vectors. Sentences are represented by summing-up the word vectors that contain. The pagerank algorithm is applied to rank sentences using the cosine similarity as a distance measure between sentences vectors. The top N highly ranked sentences are selected to build the summary. For the evaluation, we created a corpus of 200 biomedical papers downloaded from the Biomed Central full-text database. We used a pre-trained Word2vec model of word vectors generated from a combination of PubMed, PMC, and recent English Wikipedia dump texts. We compared our method with four other summarizers using: ROUGE-1, ROUGE-2, ROUGE-3, and ROUGE-SU4 metrics by evaluating the generated summaries with the abstracts of papers. Our summarizer achieved an improvement of 3.48%, 7.68%, 9.76%, and 3.47% respectively against the second-ranked summarizer.

关键词： Biomedical text summarization Word embedding Word2vec pagerank algorithm ROUGE metrics

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A recommender model based on strong and weak social Ties: A Long-tail distribution perspective

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 184卷 115483-115483页

作者： He, Wei-jun Ai, Dan-xiang Wu, ChienHsing Guangdong Univ Technol Sch Management 161 Yinglong Rd Guangzhou 510520 Guangdong Peoples R China Natl Univ Kaohsiung Dept Informat Management 700 Kaohsiung Univ Rd Kaohsiung 81148 Taiwan

A collaborative filtering recommender model for E-commerce users (CF-S&WLT) is proposed that incorporates an improved pagerank algorithm, strong and weak social ties and long-tail distribution items. The main design feature of the CF-S&WLT model is the introduction of incentive coefficients for long-tail items based on triadic closure regulation. It was compared to three alternative models in terms of accuracy (precision and F-measure), diversity, and novelty of returned Top-N items. Results reveal that CF-S&WLT improved diversity and novelty by 20.1% and 3.8% respectively, but reduced precision by 3.7% based on a first dataset (DS1), and enhanced diversity (14.2%) and novelty (18.3%) while reducing precision cost by 4.6% using dataset DS2. Overall, the proposed CF-S&WLT model performs better than other models with respect to diversity and novelty while maintaining acceptable levels of accuracy.

关键词： Recommender systems Social ties pagerank algorithm Long-tail distributions

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A Novel Social Recommendation Method Fusing User's Social Status and Homophily Based on Matrix Factorization Techniques

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IEEE ACCESS 2019年 7卷 18783-18798页

作者： Chen, Rui Hua, Qingyi Wang, Bo Zheng, Min Guan, Weili Ji, Xiang Gao, Quanli Kong, Xiangjie Northwest Univ Sch Informat Sci & Technol Xian 710127 Shaanxi Peoples R China Zhengzhou Univ Aeronaut Sch Comp Sci Zhengzhou 450046 Henan Peoples R China Xian Univ Posts & Telecommun Sch Comp Sci Xian 710121 Shaanxi Peoples R China Engn Univ Peoples Armed Police Force Sch Informat Engn Xian 710038 Shaanxi Peoples R China Hewlett Packard Enterprise Singapore 918112 Singapore Xian Polytech Univ Coll Comp Sci Xian 710048 Shaanxi Peoples R China Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China

As one of the most successful recommendation techniques, collaborative filtering provides a useful recommendation by associating an active user with a crowd of users who share the same interests. Although some achievements have been achieved both in theory and practice, the efficiency of recommender systems has been negatively affected by the problems of cold start and data sparsity recently. To solve the above problems, the trust relationship among users is employed into recommender systems to build a learning model to further promote the prediction quality and users' satisfaction. However, most of the existing social networks-based recommendation algorithms fail to take into account the fact that users with different levels of trust and backgrounds, that is, user's social status and homophily have different degrees of influence on their friends. In this paper, a novel social matrix factorization-based recommendation method is proposed to improve the recommendation quality by fusing user's social status and homophily. User's social status and homophily play important roles in improving the performance of recommender systems. We first build a user's trust relationship network based on user social relationships and the rating information. Then, the degree of trust is calculated through the trust propagation method and the pagerank algorithm. Finally, the trust relationship is integrated into the matrix factorization model to accurately predict unknown ratings. The proposed method is evaluated using real-life datasets including the Epinions and Douban datasets. The experimental results and comparisons demonstrate that the proposed approach is superior to the existing social networks-based recommendation algorithms.

关键词： Recommender systems collaborative filtering matrix factorization pagerank algorithm trust networks

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Stages of User Engagement on Social Commerce Platforms: Analysis with the Navigational Clickstream Data

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INTERNATIONAL JOURNAL OF ELECTRONIC COMMERCE 2019年第2期23卷 179-211页

作者： Kumar, Ashish Salo, Jari Li, Hongxiu Aalto Univ Sch Business Dept Mkt Espoo Finland Univ Helsinki Mkt Helsinki Finland Tampere Univ Technol Dept Ind & Informat Management Tampere Finland

Social commerce platforms have gained prominence in e-commerce, as social media has become an integral part of users' online activities. Therefore, firms have been either developing or utilizing social commerce platforms to increase user engagement by adding social shopping facility onto their electronic commerce platforms. However, managing user engagement and user interaction becomes complex when e-commerce platforms are transformed into social commerce platforms. In this study, we operationalize four distinct stages of the social commerce platform, namely, social identification, social interaction, social shopping, and transaction based on salience theory. Using clickstream data, we empirically measure user engagement in these four states by modeling users' incidence and time spent. Drawing from the pagerank algorithm, we capture the importance of ranking and distance on user engagement. The model also accounts for the effects of situational variables such as weekend;holiday;time of day;and user characteristics, such as gender and social media setting. Our results suggest that ranking and distance have significant effects on users' incidence as well as time spent on social commerce platforms. The insights from this study can be helpful in designing the social commerce platform effectively using only the customers' path navigational clickstream data from the parent social commerce platform.

关键词： Clickstream data Dijkstra's shortest path algorithm hierarchical Bayesian method multivariate type-2 Tobit online communities online platforms online shopping pagerank algorithm social commerce platforms

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Combining Hyperlink Structure and Content of Webpage for Personalization of Search Engine 3rd

Combining Hyperlink Structure and Content of Webpage for Per...

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3rd International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Anjusha, I. T. Nizar, M. Abdul Coll Engn Trivandrum Thiruvananthapuram Kerala India

ISBN: (纸本)9789811317477;9789811317460

Internet contains huge amount of information, and people explore it for satisfying their information needs. Search engine is the tool used to look for information on the Internet. But generic search engines are not adaptable to user's specific needs and interests. Search engine personalization was introduced as a solution to this problem. Personalization is the process of retrieving important webpages for people with different information goals on the basis of their individual interests and context of their activities. Content-based personalization systems returns webpages that are relevant to user interest profile. But it may not ensure quality of webpage. Hyperlink-based systems return webpages with high quality. But it may also return webpages that are only peripherally related to the user interests. In this paper, we present a proposal for search technique that combines link structure and page contents to produce high-quality, user-relevant webpages. Our experiments reveal that the proposed system produces better search results than generic search engine and content-based personalization system.

关键词： pagerank algorithm Personalization User profiling Webpage re-ranking Search engine

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Research on the Nodes Importance in Complex Networks Based on Community Partition 19

Research on the Nodes Importance in Complex Networks Based o...

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2nd International Conference on Big Data Technologies (ICBDT) / 3rd International Conference on Business Information Systems Workshop (ICBIS)

作者： Chen, Feng Huang, Yajing Beijing Forestry Univ Sch Sci China 35 Tsinghua East Rd Beijing Peoples R China

ISBN: (纸本)9781450371926

In complex network analysis, the importance of nodes has always been one of the main research fields of graph theory and the most common calculation method is the pagerank algorithm. The existing research is mainly based on the whole network level to rank all nodes, which will bring a huge amount of computation when the network is quite large. In fact, a node is more likely to be affected by the nodes that are closely connected to it, rather than by the nodes that have the highest importance in the entire network. So, it is necessary to decompose the networks into communities, whose main feature is that the nodes within a community are closely connected, while the nodes between different communities are sparsely connected. In this paper, the classical Louvain algorithm will be applied to achieve community partition, for the further study of nodes importance. Firstly, the statistical characteristics of pagerank values of nodes between different communities are studied for four different types of networks: regular network, random network, small-world network and scale-free network. Secondly, a real enterprise investment network is studied based on a large number of Chinese enterprise investment data. The nodes in the same community are extracted to form a sub-network and a new set of pagerank values will be calculated for these nodes. Comparison results of two sets of pagerank values of the same group of nodes show that the rank of nodes importance in the whole network is highly consistent with that in the sub-network. For the network model established by big data, community partition method can greatly reduce the computational load of nodes important and simplify the research process. The conclusion of this paper is applicable to large-scale complex networks in reality, and has vital practical significance.

关键词： Complex network Community partition Node importance Louvain algorithm pagerank algorithm

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The attack efficiency of pagerank and HITS algorithms on complex networks

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2019年第3期11卷 306-314页

作者： Su, Yangqian Yi, Yunfei Qin, Jun Guangxi Teachers Educ Univ Coll Comp & Informat Engn Nanning 530023 Peoples R China Hechi Univ Coll Comp & Informat Engn Yizhou 546300 Peoples R China South Cent Univ Nationalities Coll Comp Sci Wuhan 430074 Hubei Peoples R China

With the growing scale of networks, network attack strategies with high attack efficiency and computational efficiency are becoming more and more important. Various attack strategies with various sorting methods have been proposed. However most of them neglected the computational efficiency. Inspired by the high computational efficiency of pagerank and HITS algorithms that used in web pages network, this paper introduces those two algorithms to the network attack separately and explores the feasibility of those two new strategies. The initial experiments choose degree and BC attack as the contrast group, and compare the attack efficiency in six virtual networks. The results indicate that considering the computational efficiency and attack efficiency simultaneously, pagerank strategy has a better attack performance than other compared strategies. Initial discussions about the results are also given.

关键词： pagerank algorithm HITS algorithm attack strategy

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Comparative Study on Chinese and Global OLED Industry Based on Patent Data

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IEEE ACCESS 2018年 6卷 72381-72391页

作者： Sun, Bing Wang, Hongying Harbin Engn Univ Sch Econ & Management Harbin 150001 Heilongjiang Peoples R China

Against the background of intelligent manufacturing, the development of chip technology has been widely discussed by scholars. The Chinese chip industry is constantly hit upon in the international background, and the reasons behind this exploration are particularly important for the future development of chip technology in the country. To solve this problem, the paper chooses to study the typical OLED technology in the chip industry. This paper uses 14979 patent data of OLED technology, from 1900 to 2017, as the research object. Data are taken from the global patent statistics database (PATSTAT). From the perspective of the annual application numbers and main research content, it is concluded that Chinese OLED technology has been a late starter, with companies mainly choosing the road of importing equipment OLED technology lacks specific direction in view of the mastered technical patents, and the related technology is mostly in the peripheral activities. Therefore, OLED technology cannot enter the core area of this field. In this paper, the leading technology identification model, based on a pagerank algorithm, is used to identify the leading technology in the global and domestic OLED patents, and we compare the patents' holders. It is concluded that the Chinese OLED industry cannot occupy a favorable position on the international stage;some research institutes in China have R&D ability, but they lack production facilities. The development of industrialization restricts the sustainable development of related technologies. In view of the above results, this paper provides suggestions from the perspective of researchers, related enterprises, and government and patent protection agencies, in order to promote the development of the Chinese OLED industry and to play a referential role in the growth of China's chip industry.

关键词： Chip industry OLED technology comparative analysis pagerank algorithm dominant technology

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Financial resource integration algorithm of virtual enterprise in big data environment

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第4期35卷 4183-4194页

作者： Wu, Guoqiang Saghir, V Huainan Normal Univ Sch Econ & Management Huainan 232038 Peoples R China NYU Courant Inst Math Sci New York NY USA

The current resource integration algorithm lacks the consideration of users' needs, which can cause high violation of service-level agreement and poor data quality after integration. It affects the energy consumption and service quality of data center. To address this problem, a financial resource integration algorithm of virtual enterprise based on improved artificial bee colony in big data environment is proposed in this paper. The improved pagerank algorithm is used to extract the financial resource of virtual enterprise. The extracted resource is transformed. From the unified data resource centralization after transformation, service resources that satisfy users' needs and constraints are selected and combined. An improved artificial bee colony algorithm is applied to dynamically integrate service resources for different needs. Experimental results show that the proposed algorithm can effectively reduce the energy consumption of the data center, improve the data quality and user service satisfaction. The advantages and feasibility of the proposed algorithm in the integration of virtual enterprise financial resources under the big data environment are verified.

关键词： Big data environment virtual resource enterprise financial resource resource integration pagerank algorithm

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Detecting opinion leaders in online social networks using HybridRank algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第1期35卷 513-522页

作者： Qiu Liqing Dai Jinlong Liu Haiyan Wen Yan Shandong Univ Sci & Technol Coll Comp Sci & Engn Shandong Prov Key Lab Wisdom Mine Informat Techno Qingdao Peoples R China

Opinion leaders are those users who have great influence in social networks. It is significant to detect opinion leaders for the study on social networks and other applications. According to the key idea of the pagerank algorithm, a novel algorithm called HybridRank is proposed, taking into account topic-sensitive analysis and temporal characteristics. Our two major contributions are twofold: (1) topic-sensitive analysis is conducted to obtain the clusters in social networks;(2) temporal analysis is proposed to investigate the dynamics of the user's influence over the time. We also provide impressive experimental analysis on a real dataset grabbed from Chinese Sina BBS, showing that the proposed HybridRank algorithm outperforms various related approaches.

关键词： Opinion leader pagerank algorithm topic-sensitive analysis temporal characteristics social networks

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