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CNAVER: A Content and Network-based Academic VEnue Recommender system
Knowledge-Based Systems ( IF 7.2 ) Pub Date : 2019-10-17 , DOI: 10.1016/j.knosys.2019.105092
Tribikram Pradhan , Sukomal Pal

The phenomenon of rapidly developing academic venues poses a significant challenge for researchers: how to recognize the ones that are not only in accordance with one’s scholarly interests but also of high significance? Often, even a high-quality paper is rejected because of a mismatch between the research area of the paper and the scope of the journal. Recommending appropriate scholarly venues to researchers empowers them to recognize and partake in important academic conferences and assists them in getting published in impactful journals. A venue recommendation system becomes helpful in this scenario, particularly when exploring a new field or when further choices are required. We propose CNAVER: A Content and Network-based Academic VEnue Recommender system. It provides an integrated framework employing a rank-based fusion of paper-paper peer network (PPPN) model and venue-venue peer network (VVPN) model. It only requires the title and abstract of a paper to provide venue recommendations, thus assisting researchers even at the earliest stage of paper writing. It also addresses cold start issues such as the involvement of an inexperienced researcher and a novel venue along with the problems of data sparsity, diversity, and stability. Experiments on the DBLP dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of precision, nDCG, MRR, accuracy, Fmeasuremacro, average venue quality, diversity, and stability.



中文翻译:

CNAVER:基于内容和网络的学术Venue推荐系统

学术场所快速发展的现象给研究人员带来了巨大挑战:如何识别不仅符合学术兴趣而且具有重要意义的场所?通常,即使高质量的论文也被拒绝,因为论文的研究领域与期刊范围不匹配。向研究人员推荐适当的学术场所,使他们能够认识并参加重要的学术会议,并帮助他们在有影响力的期刊上发表论文。在这种情况下,场所推荐系统会很有用,特别是在探索新领域或需要进一步选择时。我们提出了CNAVER:基于内容和网络的学术Venue推荐系统。它提供了一个集成框架,该框架采用了基于等级的纸对等网络(PPPN)和场馆对等网络(VVPN)模型的融合。它仅需要论文的标题和摘要即可提供场所建议,从而即使在论文写作的最早阶段也能为研究人员提供帮助。它还解决了冷启动问题,例如缺乏经验的研究人员和新颖的场所,以及数据稀疏性,多样性和稳定性问题。在DBLP数据集上进行的实验表明,我们提出的方法在精度,nDCG,MRR,准确性,它还解决了冷启动问题,例如缺乏经验的研究人员和新颖的场所,以及数据稀疏性,多样性和稳定性问题。在DBLP数据集上进行的实验表明,我们提出的方法在精度,nDCG,MRR,准确性,它还解决了冷启动问题,例如缺乏经验的研究人员和新颖的场所,以及数据稀疏性,多样性和稳定性问题。在DBLP数据集上进行的实验表明,我们提出的方法在精度,nDCG,MRR,准确性,F-Ë一种sü[RË一种C[RØ,平均会场质量,多样性和稳定性。

更新日期:2020-01-16
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