基于局部模型加权融合的Top-N电影推荐算法

计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 439-444.

基于局部模型加权融合的Top-N电影推荐算法

汤颖¹, 孙康高¹, 秦绪佳¹, 周建美²

浙江工业大学计算机科学与技术学院杭州310023¹
南通大学计算机科学与技术学院江苏南通226019²

出版日期:2019-02-26 发布日期:2019-02-26
作者简介:汤颖(1977-),女,博士,副教授,硕士生导师,CCF会员,主要研究方向为信息可视化、数据挖掘和计算机图形图像,E-mail:ytang@zjut.edu.cn;孙康高(1992-),男,硕士生,主要研究方向为数据挖掘、推荐系统;秦绪佳(1968-),男,博士,教授,博士生导师,CCF会员,主要研究方向为数据可视化、计算机图形学;周建美(1977-),女,硕士,副教授,主要研究方向为信息安全、网络数据库。
基金资助:
本文受国家自然科学基金(71571160,61672462)资助。

Local Model Weighted Ensemble for Top-N Movie Recommendation

TANG Ying¹, SUN Kang-gao¹, QIN Xu-jia¹, ZHOU Jian-mei²

School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China¹
School of Computer Science and Technology,Nantong University,Nantong,Jiangsu 226019,China²

Online:2019-02-26 Published:2019-02-26

摘要/Abstract

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。

关键词: 模型融合, 推荐系统, 稀疏线性模型, 主题模型

Abstract: 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.

Key words: Model ensemble, Recommendation system, Sparse linear model, Topic model

中图分类号:

TP301

汤颖, 孙康高, 秦绪佳, 周建美. 基于局部模型加权融合的Top-N电影推荐算法[J]. 计算机科学, 2018, 45(11A): 439-444. https://doi.org/

TANG Ying, SUN Kang-gao, QIN Xu-jia, ZHOU Jian-mei. Local Model Weighted Ensemble for Top-N Movie Recommendation[J]. Computer Science, 2018, 45(11A): 439-444. https://doi.org/

参考文献

[1]DESHPANDE M,KARYPIS G.Item-based top-n recommendation algorithms[J].ACM Transactions on Information Systems (TOIS),2004,22(1):143-177.
[2]NING X,KARYPIS G.Slim:Sparse linear methods for top-n recommender systems[C]∥2011 IEEE 11th International Conference on Data Mining (ICDM).IEEE,2011:497-506.
[3]FUNK S.Netflix Update:Try This at Home[OL].http://si-fter.org/~simon/journal/20061211.html.
[4]KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[5]KOREN Y.Factorization meets the neighborhood:a multiface-ted collaborative filtering model[C]∥Proceedings of the 14th ACM SIGKDD international conference on Knowledge Discoveryand Data Mining.ACM,2008:426-434.
[6]KOREN Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM,2010,53(4):89-97.
[7]HU Y,KOREN Y,VOLINSKY C.Collaborative filtering for implicit feedback datasets[C]∥Eighth IEEE International Conference on Data Mining,2008(ICDM’08).IEEE,2008:263-272.
[8]KABBUR S,NING X,KARYPIS G.Fism:factored item simila-rity models for top-n recommender systems[C]∥Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2013:659-667.
[9]CREMONESI P,KOREN Y,TURRIN R.Performance of re-commender algorithms on top-n recommendation tasks[C]∥Proceedings of the Fourth ACM Conference on Recommender Systems.ACM,2010:39-46.
[10]KANG Z,PENG C,CHENG Q.Top-N Recommender System via Matrix Completion[C]∥AAAI.2016:179-185.
[11]RENDLE S,FREUDENTHALER C,GANTNER Z,et al.BPR:Bayesian personalized ranking from implicit feedback[C]∥Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence.AUAI Press,2009:452-461.
[12]O’CONNOR M,HERLOCKER J.Clustering items for collaborative filtering[C]∥Proceedings of the ACM SIGIR Workshop on Recommender Systems.UC Berkeley,1999:128.
[13]XU B,BU J,CHEN C,et al.An exploration of improving colla-borative recommender systems via user-item subgroups[C]∥Proceedings of the 21st International Conference on World Wide Web.ACM,2012:21-30.
[14]LEE J,KIM S,LEBANON G,et al.Local low-rank matrix approximation[C]∥International Conference on Machine Lear-ning.2013:82-90.
[15]LEE J,BENGIO S,KIM S,et al.Local collaborative ranking[C]∥Proceedings of the 23rd International Conference on World Wide Web.ACM,2014:85-96.
[16]CHRISTAKOPOULOU E,KARYPIS G.Local item-item mo-dels for top-n recommendation [C]∥Proceedings of the 10th ACM Conference on Recommender Systems.ACM,2016:67-74.
[17]BLEI D M,NG A Y,JORDAN M I.Latent dirichlet allocation[J].Journal of Machine Learning Research,2003,3(1):993-1022.
[18]HOFFMAN M,BACH F R,BLEI D M.Online learning for latent dirichlet allocation[C]∥Advances in Neural Information Processing Systems.2010:856-864.
[19]SEDHAIN S,MENON A K,SANNER S,et al.Autorec:Autoencoders meet collaborative filtering[C]∥Proceedings of the 24th International Conference on World Wide Web.ACM,2015:111-112.
[20]CHENG H T,KOC L,HARMSEN J,et al.Wide & deep lear-ning for recommender systems[C]∥Proceedings of the 1st Workshop on Deep Learning for Recommender Systems.ACM,2016:7-10.

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