[Objective] This paper creates a musical feature system based on multi-source information, aiming to address the cold start issue facing music recommendation and provide personalized services. [Methods] We proposed a two-stage model with multi-source information fused by neural network algorithm. Then, we built the musical feature system and predicted the potential factor vectors of music. Finally, we generated the TopN recommendation list for the users. [Results] We examined our model with the Million Song Dataset. Compared with other models such as CNN, the F1 value was improved by 9.13%, and the RMSE, MAE values were reduced by 8.08% and 3.91%, respectively. [Limitations] Our new method encounters more limits than the end-to-end training ones. And training with the Mel-frequency spectrum demands much more memory. [Conclusions] The proposed model improves the performance of music recommendation services.
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