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.

中文翻译：

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使用因子分解机器学习方法进行音乐推荐

将用户数据挖掘引入到模型构建过程中，并通过因子分解机（FM）学习方法分析各种影响因素来分解用户行为。在推荐筛选阶段，将协作过滤推荐组合以筛选推荐候选集。基于用户的协作过滤（CF）的思想可作为参考，以获取类似用户喜欢的音乐作品。另一方面，我们从基于项目的CF中学习，它可以确保候选集覆盖用户的偏好。首先，通过动态兴趣模型预测用户的兴趣值。然后，充分考虑了诸如冷启动和热物料处理等常见问题。将频繁模式增长算法与基于协同过滤推荐算法和基于内容的推荐算法的关联规则算法进行比较，证明了该算法的优越性及其在应用推荐后解决推荐问题中的作用。音乐数据在数据库中的数据转换有效地提高了挖掘的效率和准确性。根据本文所述算法的实现，证明了用于推荐用户满意度的音乐推荐结果的准确性。推荐的音乐确实是可行和实用的。证明了该算法的优越性及其在应用推荐后解决推荐问题中的作用。音乐数据在数据库中的数据转换有效地提高了挖掘的效率和准确性。根据本文所述算法的实现，证明了用于推荐用户满意度的音乐推荐结果的准确性。推荐的音乐确实是可行和实用的。证明了该算法的优越性及其在应用推荐后解决推荐问题中的作用。音乐数据在数据库中的数据转换有效地提高了挖掘的效率和准确性。根据本文所述算法的实现，证明了用于推荐用户满意度的音乐推荐结果的准确性。推荐的音乐确实是可行和实用的。