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dc.contributor.authorLiu, Peng
dc.contributor.authorZhang, Lemei
dc.contributor.authorGulla, Jon Atle
dc.date.accessioned2020-01-15T08:56:56Z
dc.date.available2020-01-15T08:56:56Z
dc.date.created2019-05-05T12:27:27Z
dc.date.issued2019
dc.identifier.citationLecture Notes in Computer Science (LNCS). 2019, 11052 LNAI 691-708.nb_NO
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11250/2636328
dc.description.abstractWith the rapid proliferation of online social networks, personalized social recommendation has become an important means to help people discover useful information over time. However, the cold-start issue and the special properties of social networks, such as rich temporal dynamics, heterogeneous and complex structures with millions of nodes, render the most commonly used recommendation approaches (e.g. Collaborative Filtering) inefficient. In this paper, we propose a novel multi-granularity dynamic network embedding (m-DNE) model for the social recommendation which is capable of recommending relevant users and interested items. In order to support online recommendation, we construct a heterogeneous user-item (HUI) network and incrementally maintain it as the social network evolves. m-DNE jointly captures the temporal semantic effects, social relationships and user behavior sequential patterns in a unified way by embedding the HUI network into a shared low dimensional space. Meanwhile, multi-granularity proximities which include the second-order proximity and the community-aware high-order proximity of nodes, are introduced to learn more informative and robust network representations. Then, with an efficient search method, we use the encoded representation of temporal contexts to generate recommendations. Experimental results on several real large-scale datasets show its advantages over other state-of-the-art methods.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleLearning multi-granularity dynamic network representations for social recommendationnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber691-708nb_NO
dc.source.volume11052 LNAInb_NO
dc.source.journalLecture Notes in Computer Science (LNCS)nb_NO
dc.identifier.doi10.1007/978-3-030-10928-8_41
dc.identifier.cristin1695633
dc.description.localcodeThis is a post-peer-review, pre-copyedit version of an article. Locked until 23.1.2020 due to copyright restrictions. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-10928-8_41nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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