Matches in SemOpenAlex for { <https://semopenalex.org/work/W3175655318> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W3175655318 endingPage "115359" @default.
- W3175655318 startingPage "115359" @default.
- W3175655318 abstract "• A novel neural model is proposed for global citation recommendation task. The proposed model is essentially a combination of three neural networks with latent variables. • A novel author embedding method that uses limited localized neighbors is developed. The extensibility of this method is verified. • The learning algorithm of the proposed neural model is detailedly derived. • The time complexity for the learning algorithm is analyzed. • Extensive experiments manifest the superiority of the proposed model. Citation recommendation is an effective and efficient way to facilitate authors finding desired references. This paper presents a novel neural network based model, called gated relational probabilistic stacked denoising autoencoder with localized author (GRSLA) embedding, for global citation recommendation task. Our model is comprised of two modules with different neural network architecture. For each citing and cited papers, we use a gated paper embedding module, which is extended from probabilistic stacked denoising autoencoder (PSDAE) by adding gated units, to obtain their paper vectors. The added gated units are able to utilize text information of cited paper to refine the vector representation of citing paper in multiple semantic levels. For an author in papers, we first apply topic model to obtain his/her semantic neighbors, and then use a localized author embedding (LAE) module to excavate author vector representation from semantic and explicit neighbors. Unlike most graph convolutional network (GCN) based methods, the LAE module is able to avoid computing global Laplacian in whole graph by taking limited neighbors. Moreover, the LAE module can also be stacked to absorb more neighbors, which makes our model have high extendibility. Based on the generation process of GRSLA, we also derive a learning algorithm of our model by maximum a posteriori (MAP) estimation. We conduct experiments on the AAN, DBLP and CORD-19 datasets, and the results show that GRSLA model works well than previous global citation recommendation methods." @default.
- W3175655318 created "2021-07-05" @default.
- W3175655318 creator A5008056593 @default.
- W3175655318 creator A5012088434 @default.
- W3175655318 creator A5025394263 @default.
- W3175655318 creator A5071392629 @default.
- W3175655318 creator A5072440587 @default.
- W3175655318 creator A5076034357 @default.
- W3175655318 date "2021-12-01" @default.
- W3175655318 modified "2023-09-24" @default.
- W3175655318 title "Gated relational stacked denoising autoencoder with localized author embedding for global citation recommendation" @default.
- W3175655318 cites W103340358 @default.
- W3175655318 cites W2047534799 @default.
- W3175655318 cites W2062340319 @default.
- W3175655318 cites W2101599977 @default.
- W3175655318 cites W2145677303 @default.
- W3175655318 cites W2165636119 @default.
- W3175655318 cites W2178628967 @default.
- W3175655318 cites W2733658935 @default.
- W3175655318 cites W2754625913 @default.
- W3175655318 cites W2765431210 @default.
- W3175655318 cites W2787905871 @default.
- W3175655318 cites W2802966978 @default.
- W3175655318 cites W2905586594 @default.
- W3175655318 cites W2918649722 @default.
- W3175655318 cites W2952667294 @default.
- W3175655318 cites W2972437235 @default.
- W3175655318 cites W2982667460 @default.
- W3175655318 cites W3038111897 @default.
- W3175655318 cites W3043016077 @default.
- W3175655318 cites W4251326898 @default.
- W3175655318 doi "https://doi.org/10.1016/j.eswa.2021.115359" @default.
- W3175655318 hasPublicationYear "2021" @default.
- W3175655318 type Work @default.
- W3175655318 sameAs 3175655318 @default.
- W3175655318 citedByCount "5" @default.
- W3175655318 countsByYear W31756553182022 @default.
- W3175655318 countsByYear W31756553182023 @default.
- W3175655318 crossrefType "journal-article" @default.
- W3175655318 hasAuthorship W3175655318A5008056593 @default.
- W3175655318 hasAuthorship W3175655318A5012088434 @default.
- W3175655318 hasAuthorship W3175655318A5025394263 @default.
- W3175655318 hasAuthorship W3175655318A5071392629 @default.
- W3175655318 hasAuthorship W3175655318A5072440587 @default.
- W3175655318 hasAuthorship W3175655318A5076034357 @default.
- W3175655318 hasConcept C101738243 @default.
- W3175655318 hasConcept C108583219 @default.
- W3175655318 hasConcept C136764020 @default.
- W3175655318 hasConcept C153180895 @default.
- W3175655318 hasConcept C154945302 @default.
- W3175655318 hasConcept C163294075 @default.
- W3175655318 hasConcept C23123220 @default.
- W3175655318 hasConcept C2778805511 @default.
- W3175655318 hasConcept C41008148 @default.
- W3175655318 hasConcept C41608201 @default.
- W3175655318 hasConceptScore W3175655318C101738243 @default.
- W3175655318 hasConceptScore W3175655318C108583219 @default.
- W3175655318 hasConceptScore W3175655318C136764020 @default.
- W3175655318 hasConceptScore W3175655318C153180895 @default.
- W3175655318 hasConceptScore W3175655318C154945302 @default.
- W3175655318 hasConceptScore W3175655318C163294075 @default.
- W3175655318 hasConceptScore W3175655318C23123220 @default.
- W3175655318 hasConceptScore W3175655318C2778805511 @default.
- W3175655318 hasConceptScore W3175655318C41008148 @default.
- W3175655318 hasConceptScore W3175655318C41608201 @default.
- W3175655318 hasFunder F4320324173 @default.
- W3175655318 hasLocation W31756553181 @default.
- W3175655318 hasOpenAccess W3175655318 @default.
- W3175655318 hasPrimaryLocation W31756553181 @default.
- W3175655318 hasRelatedWork W2292254049 @default.
- W3175655318 hasRelatedWork W2592385986 @default.
- W3175655318 hasRelatedWork W2785535669 @default.
- W3175655318 hasRelatedWork W2897995864 @default.
- W3175655318 hasRelatedWork W2910484851 @default.
- W3175655318 hasRelatedWork W2966657595 @default.
- W3175655318 hasRelatedWork W2998168123 @default.
- W3175655318 hasRelatedWork W3090006671 @default.
- W3175655318 hasRelatedWork W4281924768 @default.
- W3175655318 hasRelatedWork W4287995534 @default.
- W3175655318 hasVolume "184" @default.
- W3175655318 isParatext "false" @default.
- W3175655318 isRetracted "false" @default.
- W3175655318 magId "3175655318" @default.
- W3175655318 workType "article" @default.