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dc.contributor.authorSymeonidis P
dc.contributor.authorPerentis C
dc.contributor.editor
dc.date.accessioned2019-03-08T07:40:52Z
dc.date.available2019-03-08T07:40:52Z
dc.date.issued2014
dc.identifier.isbn978-3-662-44844-1
dc.identifier.issn0302-9743
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-662-44845-8_10
dc.identifier.urihttp://link.springer.com/chapter/10.1007/978-3-662-44845-8_10
dc.identifier.urihttp://hdl.handle.net/10863/9008
dc.description.abstractOnline social networks like Facebook recommend new friends to users based on an explicit social network that users build by adding each other as friends. The majority of earlier work in link prediction infers new interactions between users by mainly focusing on a single network type. However, users also form several implicit social networks through their daily interactions like commenting on people's posts or rating similarly the same products. Prior work primarily exploited both explicit and implicit social networks to tackle the group/item recommendation problem that recommends to users groups to join or items to buy. In this paper, we show that auxiliary information from the user-item network fruitfully combines with the friendship network to enhance friend recommendations. We transform the well-known Katz algorithm to utilize a multi-modal network and provide friend recommendations. We experimentally show that the proposed method is more accurate in recommending friends when compared with two single source path-based algorithms using both synthetic and real data sets.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relationEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2014 ; Nancy : 15.9.2014 - 19.9.2014
dc.relation.ispartofseriesLecture Notes in Computer Science;
dc.rights
dc.subjectFriend recommendationen_US
dc.subjectLink predictionen_US
dc.titleLink prediction in multi-modal social networksen_US
dc.typeBook chapteren_US
dc.date.updated2019-03-08T03:03:29Z
dc.publication.titleMachine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III
dc.language.isiEN-GB
dc.description.fulltextopenen_US


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