电影推荐系统中基于图的协同过滤算法

A Graph-based Collaborative Filtering Algorithm in Movie Recommendation System

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。

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.