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This paper proposes a content-based artist recommendation framework which learns relationships between users' preference and music contents through ordinal regression. In particular, an artist is characterized by the parameters of its corresponding acoustical model which is adapted from a universal background model. These artist-specific acoustic features together with their preference rankings are then used as input vectors for the proposed order preserving projection (OPP) algorithm which tries to find a suitable subspace such that the desired ranking order of the data after projection can be kept as much as possible. The proposed linear OPP can be kernelized to learn the nonlinear relationship between music contents and users' artist rank orders. Under the proposed framework of kernelized OPP (KOPP), we can derive the nonlinear relationship and, more importantly, efficiently fuse acoustic and symbolic features obtained from the artist recommended meta-data. Experimental results demonstrate that OPP attains comparable results with those obtained with a conventional ordinal regression method, Prank. Moreover, by exploring the nonlinear relationship among training examples and combining acoustic and symbolic features, KOPP outperforms previous approaches to artist recommendation.