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Friend Recommendation Using Probabilistic Matrix Co-Factorization

Title
Friend Recommendation Using Probabilistic Matrix Co-Factorization
Authors
LeThiThanhHuyen
Date Issued
2012
Publisher
포항공과대학교
Abstract
Nowadays, 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 which can 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.
URI
http://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001390750
http://oasis.postech.ac.kr/handle/2014.oak/1657
Article Type
Thesis
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