Patent ID: 8224818

Claim:
A music recommendation method, comprising: providing a plurality of music items, and a rating data matrix, wherein the rating data matrix comprises a plurality of music item identifications of the music items, a plurality of ratings belonging to each of the music items, and a plurality of user identifications of a plurality of users providing the ratings; classifying the ratings of each of the music items into positive ratings and negative ratings in accordance with a predetermined rating threshold; performing a pre-processing phase to transform the music items into a plurality of perceptual patterns in accordance with acoustical and temporal features of the music items, wherein the pre-processing phase comprises: dividing each of the music items into a plurality of sections in accordance with a predetermined time period, thereby obtaining a plurality of frames of the music items; calculating Modified Discrete Cosine Transform (MDCT) coefficients of each of the frames to extract the low-level features of each of the frames; performing a frame-based clustering step to transform the music items into a plurality of first symbolic strings in a one to one manner in accordance with the acoustical features of the music items, wherein the frame-based clustering step comprises: calculating a pearson correlation coefficient between every two of the frames, wherein the pearson correlation coefficient represents the difference of the tendency of the every two of the frames; partitioning the frames into a plurality of frame clusters in accordance with the pearson correlation coefficient, wherein the pearson correlation coefficient is calculated in accordance with the Modified Discrete Cosine Transform (MDCT) coefficients of the every two frames; assigning a plurality of first symbols to the frame clusters in a one to one manner so as to classify the frames; and transforming the music items into the first symbolic strings in accordance with the types of the frames, wherein each of the first symbolic strings is represented by a sequence code composed of at least two of the first symbols; and performing a sequence-based clustering step to transform the first symbolic strings into a plurality of second symbolic strings in a one to one manner in accordance with the temporal features of the music items; and performing a prediction phase to determine an interest value of each of a plurality of target music items for an active user in accordance with the perceptual patterns, and generate a music recommendation list in accordance with the interest values of the target music items, wherein the target music items are the music items not provided a rating by the active user, and the music recommendation list comprises the target items arranged in accordance with the interest values, and thus the music recommendation list is provided as a reference for the active user to select one of the target items.