Abstract
The user data mining was introduced into the model construction process, and the user behavior was decomposed by analyzing various influencing factors through the factorization machine (FM) learning method. In the recommendation screening stage, the collaborative filtering recommendation is combined to screen the recommendation candidate set. The idea of user‐based collaborative filtering (CF) is used for reference to obtain music works favored by similar users. On the other hand, we learn from item‐based CF, which ensures that the candidate set covers user preference. Firstly, the user’s interest value is predicted by using dynamic interest model. Then, the common problems such as cold start and hot items processing are fully considered. The frequent pattern growth algorithm is compared with the association rule algorithm based on the collaborative filtering recommendation algorithm and the content‐based recommendation algorithm, which proves the superiority of the algorithm and its role in solving the recommendation problem after applying the recommendation. The music data in the database data conversion effectively improve the efficiency and accuracy of mining. According to the implementation of the algorithm described in this article, the accuracy of the music recommendation results used to recommend user satisfaction is proved. And the recommended music is indeed feasible and practical.
Highlights
Data mining algorithms use the results of the analysis to define the best parameters for creating a mining model, which are applied to the entire dataset to extract viable patterns and detailed statistics
We review and summarize the principles and challenges of the recommendation system used in online music and look forward to some of the technologies that may be used to improve music recommendation results in the future [3, 4]
Acoustic features are extracted from the audio query, and music recommendation is completed by content-based collaborative filtering recommendation method
Summary
Received 8 March 2021; Revised April 2021; Accepted April 2021; Published 3 May 2021. E user data mining was introduced into the model construction process, and the user behavior was decomposed by analyzing various influencing factors through the factorization machine (FM) learning method. In the recommendation screening stage, the collaborative filtering recommendation is combined to screen the recommendation candidate set. E idea of user-based collaborative filtering (CF) is used for reference to obtain music works favored by similar users. We learn from item-based CF, which ensures that the candidate set covers user preference. E frequent pattern growth algorithm is compared with the association rule algorithm based on the collaborative filtering recommendation algorithm and the content-based recommendation algorithm, which proves the superiority of the algorithm and its role in solving the recommendation problem after applying the recommendation.
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