计算机与现代化

• 算法设计与分析 • 上一篇    下一篇

电影推荐系统中基于图的协同过滤算法

  

  1. (1.中国科学院声学研究所国家网络新媒体工程技术研究中心,北京100190;2.中国科学院大学,北京100190)
  • 收稿日期:2019-03-13 出版日期:2019-11-15 发布日期:2019-11-15
  • 作者简介:郑策(1994-),女,河南南阳人,硕士研究生,研究方向:大数据分析,基于图计算的视频服务分析,E-mail: zhengce@dsp.ac.cn; 尤佳莉(1982-),女,副研究员,博士,研究方向:网络边缘智能,大数据分析和未来网络技术, E-mail: youjl@dsp.ac.cn。
  • 基金资助:
    中国科学院先导专项课题(XDC02010701); 中国科学院青年创新促进会项目(Y529111601)

A Graph-based Collaborative Filtering Algorithm in Movie Recommendation System

  1. (1. National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Science, 
    Beijing 100190, China; 2. University of Chinese Academy of Sciences, Beijing 100190, China)
  • Received:2019-03-13 Online:2019-11-15 Published:2019-11-15

摘要: 在视频服务领域,通常使用传统的协同过滤算法来解决评分数据较为稀疏的问题,而算法的视频相似度计算仅利用评分矩阵,从而造成推荐准确度较低,针对视频资源中的电影这一应用场景提出一种基于图的协同过滤算法。结合电影属性与用户偏好的关联性,将电影信息中类型、导演和演员等信息进行图元素的映射,融合图结构特点来计算影片资源的相似度。用该方法替代传统协同过滤算法中仅利用评分矩阵的相似度计算方法,在一定程度上缓解了由于数据稀疏性影响推荐准确度的问题,实验验证了该方法的有效性。

关键词: 关联性分析, 协同过滤算法, 图结构, 个性化推荐

Abstract: In order to solve the problem of sparse scoring data in the field of video service, the traditional collaborative filtering algorithm is usually used, but the video similarity calculation of the algorithm only uses score matrix, which results in low recommendution accuracy. In this paper, a graph based collaborative filtering algorithm is proposed for the scene of the movie in video resources. Combining the correlation between movie attributes and user preferences, the map elements of film information such as types, directors and actors are mapped, and the similarity of film resources is calculated by combining the features of graph structure. This method replaces the similarity calculation method of scoring matrix in traditional collaborative filtering algorithm, which alleviates the problem that the recommendation accuracy is affected by the sparse data. Experiment verifies the effectiveness of the proposed algorithm.

Key words: correlation analysis, collaborative filtering algorithm, graph structure, personalized recommendation

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