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dc.contributor.authorKefalas P
dc.contributor.authorSymeonidis P
dc.contributor.editor
dc.date.accessioned2019-03-08T08:23:27Z
dc.date.available2019-03-08T08:23:27Z
dc.date.issued2015
dc.identifier.isbn978-3-319-23780-0
dc.identifier.issn0302-9743
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-23781-7_22
dc.identifier.urihttp://link.springer.com/chapter/10.1007/978-3-319-23781-7_22
dc.identifier.urihttp://hdl.handle.net/10863/9053
dc.description.abstractRecommender systems in location-based social networks (LBSNs), such as Facebook Places and Foursquare, have focused on recommending friends or locations to registered users by combining information derived from explicit (i.e. friendship network) and implicit (i.e. user-item rating network, user-location network, etc.) subnetworks. However, previous’s work models were static, failing to capture adequately user preferences as they change over time. In this paper, we provide a novel recommendation method by incorporating the time dimension into our model through an auxiliary artificial node (i.e. session). In particular, we construct a hybrid tripartite (i.e., user, location, session) graph, which incorporates 7 different unipartite and bipartite graphs. Then, we run on it the well known Random Walk with Restart (RWR) algorithm, which randomly propagate through the network structure which has 7 differently weighted edge types (i.e., user-location, user-session, user-user, etc.) among its entities. We evaluate experimentally how RWR improve the procession of the recommendations during different time-windows against one state-of-the-art algorithm over the GeoSocialRec and the Foursquare datasets.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation5th International Conference on Model & Data Engineering (MEDI'2015) ; Rhodes : 26.9.2015 - 29.9.2015
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.rights
dc.subjectLocation recommendationen_US
dc.subjectSocial networksen_US
dc.subjectAlgorithmsen_US
dc.subjectLink predictionen_US
dc.titleRecommending friends and locations over a heterogeneous spatio-temporal graphen_US
dc.typeBook chapteren_US
dc.date.updated2019-03-08T03:00:54Z
dc.publication.titleModel and Data Engineering: 5th International Conference, MEDI 2015, Rhodes, Greece, September 26-28, 2015, Proceedings
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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