Data imputation using a trust network for recommendation via matrix factorization | Zendy

Wonseok Hwang | Zendy; Shaoyu Li | Zendy; SangWook Kim | Zendy; Kichun Lee | Zendy

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Data imputation using a trust network for recommendation via matrix factorization

Author(s) -

Wonseok Hwang,

Shaoyu Li,

SangWook Kim,

Kichun Lee

Publication year - 2018

Publication title -

computer science and information systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.244

H-Index - 24

eISSN - 2406-1018

pISSN - 1820-0214

DOI - 10.2298/csis170820003h

Subject(s) - computer science , imputation (statistics) , missing data , data mining , recommender system , matrix decomposition , machine learning , artificial intelligence , eigenvalues and eigenvectors , physics , quantum mechanics

Disclosed is a missing grade imputation device for imputing a grade on an item which a certain user does not evaluate with grades by neighbor users connected through a reliability network. The missing grade can be imputed through: a step of determining users who can reach a certain user in the forward direction of an edge and users who can reach the certain user in the forward direction of the edge as neighbor users; a step of acquiring grades by the neighbor users on an item which is not graded by the certain user from the grade matrix of the certain user; and a step of imputing the grade on the item which is not graded by the certain user by the certain user from a value calculated from the grades by the neighbor users on the item which is not graded by the certain user.

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