The music recommender system can help us extract valuable music from a huge amount of raw information, due to the increasingly severe data sparsity and cold start problems in music recommendation system, the recommendation results will be inaccurate. But the traditional algorithm cannot effectively solve these problems; the existing improved algorithm still requires specific parameters in advance due to its poor stability. In this paper, a proposition for a cold start based on community detection algorithm is proposed. By projecting the bipartite network, calculating the similarity between the User and the Item, the Louvain algorithm is used to perform community detection on the projected One-mode network, so that the new record can be updated to the original community, and then the user group is recommended for music. With higher accuracy, solid stability, and shorter running time (when compared with other ease cold start algorithms), the algorithm in the larger community can be safely applied to the music recommendation system or prediction field.
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