| 矩阵分解技术在电影推荐系统中的应用 |
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| 引用本文： | 蔡崇超,许华虎.矩阵分解技术在电影推荐系统中的应用[J].教育技术导刊,2021,20(1):174-177. |
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| 作者姓名： | 蔡崇超 许华虎 |
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| 作者单位： | 1.上海大学 计算机工程与科学学院，上海 200444；2.湖州职业技术学院?物流与信息工程学院，浙江 湖州 313000 |
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| 摘 要： | 随着大数据、移动互联网的快速发展，推荐系统成为解决网络信息过载的有力工具。为解决传统推荐系统由于没有将社交网络中用户关系考虑进去而导致的稀疏矩阵、冷启动等问题，提出一种基于矩阵分解技术的电影推荐系统算法MFMRS。该算法充分考虑到社交网络中用户之间的关系对推荐结果的影响，通过设置特征参数、损失函数、随机梯度下降等方法对推荐系统的精度进行改进。结果表明，通过应用该算法，Douban数据集的精度提升62%，Netflix数据集的精度提升51%。
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| 关 键 词： | 推荐系统 社交网络 矩阵分解 梯度下降 |
| 收稿时间： | 2020-08-18 |
Application of Matrix Factorization in Movie Recommendation System |
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| CAI Chong-chao,XU Hua-hu.Application of Matrix Factorization in Movie Recommendation System[J].Introduction of Educational Technology,2021,20(1):174-177. |
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| Authors: | CAI Chong-chao XU Hua-hu |
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| Institution: | 1. School of Computer Engineering and Science， Shanghai University， Shanghai 200444， China； 2. College of Logistic and Information Engineering， Huzhou Vocational & Technical College， Huzhou 313000， China |
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| Abstract: | With the rapid development of big data， recommendation system has become a powerful tool to solve network information overload.The main purpose of this paper is to solve the problem of sparse matrix and cold start in traditional recommendation system， which does not take the user relationship into account. In this paper， an algorithm of movie recommendation system based on matrix factorization is proposed. The algorithm fully considers the influence of the relationship between users in social network on the recommendation results， and solves the above problems by setting feature parameters， gradient descent and offset variables. The results show that the accuracy of Doublan dataset and Netflix dataset is improved by 62% and 51% respectively by using this algorithm. |
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| Keywords: | recommendation system social network matrix factorization gradient descent |
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