title: Heat conduction process on community networks as a recommendation model.
abstract: Using heat conduction mechanism on a social network we develop a systematic method to predict missing values as recommendations. This method can treat very large matrices that are typical of internet communities. In particular, with an innovative, exact formulation that accommodates arbitrary boundary condition, our method is easy to use in real applications. The performance is assessed by comparing with traditional recommendation methods using real data.
authors.affiliation: Physics Department, University of Fribourg, 1700 Fribourg, Switzerland. yi-cheng.zhang@unifr.ch
authors.forename: Yi-Cheng
authors.initials: YC
authors.lastname: Zhang
authors.forename: Marcel
authors.initials: M
authors.lastname: Blattner
authors.forename: Yi-Kuo
authors.initials: YK
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D020407:Internet; D015233:Models, Statistical; D009040:Motion Pictures; D012111:Residence Characteristics; D012931:Social Environment; D013816:Thermodynamics