Heterogeneous information network embedding based personalized query-focused astronomy reference paper recommendation

© 2018, the Authors. Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, inter-relationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database

Multi-document summarization based on sentence cluster using non-negative matrix factorization

© 2017 - IOS Press and the authors. All rights reserved. Multi-document summarization aims to produce a concise summary that contains salient information from a set of source documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence cluster using Nonnegative Matrix Tri-Factorization (NMTF). The proposed framework employs NMTF to cluster sentences using inter-type relationships among documents, sentences and terms, and incorporate the intra-type information through manifold regularization. The most informative sentences are selected from each sentence cluster to form the summary. When evaluated on the DUC2004 and TAC2008 datasets, the performance of the proposed framework is comparable with that of the top three systems