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Newly Published Scientific Papers Recommendation in Heterogeneous Information Networks

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Abstract

Millions of new research papers are published each year, making it extremely difficult for researchers to find out what they really want. Existing paper recommendation algorithms cannot effectively address the recommendation of newly published papers due to lack of historical information(e.g., citation information; view log), the so-called cold start problem. Furthermore, in most of these studies, papers are considered in homogeneous or bipartite networks. However, in a real bibliographic network, there are multiple types of objects (e.g., researchers, papers, venues, topics) and multiple types of links among these objects. In this paper, we study the problem of new paper recommendation in the heterogeneous bibliographic network, and a new method called HIPRec, i.e., meta-graph based recommendation model, is proposed to solve this problem. First, the top-K most interesting meta-paths are selected based on the training data. Secondly, a greedy method is proposed to select the most significant meta-graphs generated by merging the meta-paths, which can describe more sophisticated semantics between researchers and papers than simple meta-paths. In the meantime, meta-path and meta-graph based topological features are systematically extracted from the network. Lastly, a supervised model is used to learn the best weights associated with different topological features in deciding the researcher-new paper recommendations. We present experiments on a real bibliographic network, the DBLP network, which show the effectiveness of our approach compared to state-of-the-art new paper recommendation methods.

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Notes

  1. Positive example pairs denote that researchers are interested in the new papers, and vice versa. For example, (a1,p4) and (a2,p8) can be regarded as two positive example pairs, and (a1,p7) can be regarded as a negative example pair as shown in Fig. 1.

  2. https://aminer.org/billboard/citation

  3. http://dblp.uni-trier.de/

  4. 20 very significant venues in the areas of Data Mining, Database, Information Retrieval and Artificial intelligence.

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Acknowledgements

Research in this paper was partially supported by China National Natural Science Foundation (No.61702553) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No.17YJCZH252). Appreciation also goes to anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this paper substantially.

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Authors and Affiliations

  1. School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China

    Xiao Ma & Yin Zhang

  2. Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Jiangfeng Zeng

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  1. Xiao Ma
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  2. Yin Zhang
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  3. Jiangfeng Zeng
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Correspondence to Xiao Ma.

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Ma, X., Zhang, Y. & Zeng, J. Newly Published Scientific Papers Recommendation in Heterogeneous Information Networks. Mobile Netw Appl 24, 69–79 (2019). https://doi.org/10.1007/s11036-018-1133-9

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  • DOI: https://doi.org/10.1007/s11036-018-1133-9

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