基于局部模型加权融合的Top-N电影推荐算法  

Local Model Weighted Ensemble for Top-N Movie Recommendation

作　　者：汤颖[1] 孙康高 秦绪佳[1] 周建美[2] TANG Ying;SUN Kang-gao;QIN Xu-jia;ZHOU Jian-mei(School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China;School of Computer Science and Technology,Nantong University,Nantong,Jiangsu 226019,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023 [2]南通大学计算机科学与技术学院,江苏南通226019

出　　处：《计算机科学》2018年第B11期439-444,共6页Computer Science

基　　金：国家自然科学基金(71571160;61672462)资助

摘　　要：为了解决传统推荐算法使用单一模型无法准确捕获用户偏好的问题,将稀疏线性模型作为基本推荐模型,提出了基于用户聚类的局部模型加权融合算法来实现电影的Top-N个性化推荐。同时,为了实现用户聚类,文中利用LDA主题模型和电影的文本内容信息,提出了语义层次用户特征向量的计算方法,并基于此来实现用户聚类。在豆瓣网电影数据集上的实验验证结果表明,所提局部加权融合推荐算法提升了原始基模型的推荐效果,同时又优于一些传统的经典推荐算法,从而证明了该推荐算法的有效性。In order to solve the problem that the traditional recommendation algorithms can not accurately capture the user preference with a single model,this paper proposed a Top-N personalized recommendation algorithm based on local model weighted ensemble.This recommendation algorithm adopts user clustering to compute the local models and takes the sparse linear model as the basic recommendation model.Meanwhile,the semantic-level feature vector representation of each user was proposed based on LDA topic model and movie text content information,so as to implement user clustering.The experiments of the film data crawled from Douban show that our local model weighted ensemble recommendation algorithm enhances the recommendation quality of the original base model and outperforms some traditional classical recommendation algorithms,which demonstrates the effectiveness of the proposed algorithm.

关 键 词：推荐系统 模型融合 稀疏线性模型 主题模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]