协同过滤音乐推荐论文综述

以下是一些基于协同过滤音乐推荐的论文：

'Collaborative Filtering for Music Recommendation: A Survey' by Hao Wang, Nannan Zhao, and Xiaoming Liu. This paper provides a comprehensive survey of different collaborative filtering techniques used for music recommendation, including matrix factorization, neighborhood-based methods, and hybrid approaches.
'A Hybrid Music Recommendation System based on Collaborative Filtering and Content-based Filtering' by Xinyu Li and Zhiwen Yu. This paper proposes a hybrid music recommendation system that combines collaborative filtering with content-based filtering to improve the accuracy and diversity of recommendations.
'Matrix Factorization Techniques for Recommender Systems' by Yehuda Koren, Robert Bell, and Chris Volinsky. This classic paper introduces the use of matrix factorization for collaborative filtering in recommender systems, including music recommendation.
'Combining Collaborative Filtering and Content-based Filtering for Personalized Music Recommendation' by Jialin Liu, Yiming Liu, and Qinghua Zheng. This paper proposes a hybrid music recommendation system that combines collaborative filtering with content-based filtering and uses a genetic algorithm to optimize the weights of the two methods.
'A Comparative Study of Collaborative Filtering Algorithms for Music Recommendation' by Negin Entezari-Maleki and Ali Mohammad Zareh Bidoki. This paper compares the performance of different collaborative filtering algorithms for music recommendation, including user-based, item-based, and matrix factorization methods.

这些论文提供了不同的方法和技术，可以用于构建基于协同过滤的音乐推荐系统。