基于多维度权重动态更新的用户兴趣模型

面向个性化电影推荐领域,提出一种基于多维度权重动态更新的用户兴趣模型。将电影分成演员、导演、类别、地区和时间5个维度,分别计算电影在这些维度上的相似度。采用归一化方法将电影之间的相似度转化为用户兴趣模型中的多维度权重,并应用TF-IDF算法计算各维度中特征词的权重,从而实现电影各维度权重及其特征词权重的动态更新。利用基于内容的推荐算法,在MovieLens数据集进行实验,结果表明,该模型具有较高的推荐准确率和召回率,并且能够发现用户对电影维度的偏好,解决用户兴趣漂移问题。

User Interest Model Based on Dynamic Update of Multi-dimensional Weight

For personalized movie recommendation domain,this paper proposes a user interest model based on dynamic update for multi-dimensional weight. It divides the movie into five dimensions of actor,director,categories,area and time,respectively to calculate the similarity among these dimensions of film. It uses the normalization method to change the similarity of film into multi dimension weight of the user interest model,and calculates the weights of features of each dimension in the application of TF-IDF algorithm,in order to achieve dynamic update of the film weight and dimensions of feature weight by using content-based recommendation algorithm. In the MovieLens data set for experiment,results show that,the model has higher recommendation accuracy rate and recall rate,and can find user preferences on the film dimensions,solve the problems of user interest drift.