As science advances, the academic community has published millions of research papers. Researchers devote time and effort to search relevant manuscripts when writing a paper or simply to keep up with current research. In this paper, we consider the problem of citation recommendation by extending a set of known-to-be-relevant references. Our analysis shows the degrees of cited papers in the subgraph induced by the citations of a paper, called projection graph, follow a power law distribution. Existing popular methods are only good at finding the long tail papers, the ones that are highly connected to others. In other words, the majority of cited papers are loosely connected in the projection graph but they are not going to be found by existing methods. To address this problem, we propose to combine author, venue and keyword information to interpret the citation behavior behind those loosely connected papers. Results show that different methods are finding cited papers with widely different properties. We suggest multiple recommended lists by different algorithms could satisfy various users for a real citation recommendation system. Moreover, we also explore the fast local approximation for combined methods in order to improve the efficiency.
The vocabulary used in the citation context and in the content of papers are usually quite different. To address this problem, some works propose to use translation model, which can bridge the gap between two heterogeneous languages @cite_5 @cite_22 . Based on previous work @cite_10 @cite_32 @cite_22 , built a citation recommendation system called RefSeer http: refseer.ist.psu.edu @cite_30 which perform both topic-based global recommendations and citation-context-based local recommendations.
@cite_30 @cite_22 @cite_32 @cite_5 @cite_10
2088772104 2023930240 2122778642 2953184085
Citation Recommendation is useful for an author to find out the papers or books that can support the materials she is writing about. It is a challengeable problem since the vocabulary used in the content of papers and in the citation contexts are usually quite different. To address this problem, we propose to use translation model, which can bridge the gap between two heterogeneous languages. We conduct an experiment and find the translation model can provide much better candidates of citations than the state-of-the-art methods. Citations are important in academic dissemination. To help researchers check the completeness of citations while authoring a paper, we introduce a citation recommendation system called RefSeer. Researchers can use it to find related works to cited while authoring papers. It can also be used by reviewers to check the completeness of a paper's references. RefSeer presents both topic based global recommendation and also citation-context based local recommendation. By evaluating the quality of recommendation, we show that such recommendation system can recommend citations with good precision and recall. We also show that our recommendation system is very efficient and scalable. When we write or prepare to write a research paper, we always have appropriate references in mind. However, there are most likely references we have missed and should have been read and cited. As such a good citation recommendation system would not only improve our paper but, overall, the efficiency and quality of literature search. Usually, a citation's context contains explicit words explaining the citation. Using this, we propose a method that "translates" research papers into references. By considering the citations and their contexts from existing papers as parallel data written in two different "languages", we adopt the translation model to create a relationship between these two "vocabularies". Experiments on both CiteSeer and CiteULike dataset show that our approach outperforms other baseline methods and increase the precision, recall and f-measure by at least 5 to 10 , respectively. In addition, our approach runs much faster in the both training and recommending stage, which proves the effectiveness and the scalability of our work. While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task.