Cross-language Citation Recommendation via Publication Content and Citation Representation Fusion

While citation recommendation can be important for scholars, unfortunately, because of language barrier, some scientists cannot efficiently retrieve and consume the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Publication Content and Citation Representation Fusion (PCCRF). PCCRF can learn a representation function by mapping the publications, from various languages and repositories, to a low-dimensional joint embedding space from both content semantic and citation relation viewpoints. The proposed method can optimize the publication representations by maximizing the likelihood of observing network neighborhoods (which are generated by a semi-supervised random walk algorithm) of publications. Experimental results show that the proposed method can be promising for cross-language citation recommendation.