Addressing cold-start problem in music recommendation with HMM-based Collaborative Profile Space analysis
نویسنده
چکیده
In this paper, Collaborative Profile Space analysis, a novel approach based on HMM, is proposed to tackle the “new item” cold start problem in music recommendation task. By calculating the probability of generating a particular song from a set of trained HMMs, coordinates in the Collaborative Profile Space is defined, and used in the classifiers. With self-collected dataset and evaluation strategy, it is shown that the proposed method outperforms the traditional music classification approach in the application of recommendation.
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