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dc.contributor.authorLeThiThanhHuyenen_US
dc.date.accessioned2014-12-01T11:48:18Z-
dc.date.available2014-12-01T11:48:18Z-
dc.date.issued2012en_US
dc.identifier.otherOAK-2014-01155en_US
dc.identifier.urihttp://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001390750en_US
dc.identifier.urihttp://oasis.postech.ac.kr/handle/2014.oak/1657-
dc.descriptionMasteren_US
dc.description.abstractNowadays, the number of users on social network sites or e-commercial sites becomes much larger and users’ information on those sites are normally heterogeneous, so friend recommendation becomes more and more an important issue. Many researches have been proposed such as the graph-based approach or content-based approach or hybrid ones, however the model-based method is few, especially the one can utilize a variety of user information. Advancing previous work, in this thesis we present a novel model-based algorithm whichcan incorporate both the friendship graph and the user rating matrix to learn sensible user descriptors for making friend recommendations. Then, for the experiments we use the benchmark Filmtipset dataset and prove that our algorithm outperforms the base-line methods.en_US
dc.languageengen_US
dc.publisher포항공과대학교en_US
dc.rightsBY_NC_NDen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/kren_US
dc.titleFriend Recommendation Using Probabilistic Matrix Co-Factorizationen_US
dc.typeThesisen_US
dc.contributor.college일반대학원 정보전자융합공학부en_US
dc.date.degree2012- 8en_US
dc.contributor.departmentPohang University of Science and Technnologyen_US

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