Abstract:Collaborative filtering,as the core technology of recommendation systems,is currently facing the sparsity problem of rating data.This can be effectively solved through integrating item text information.However,current methods focus on extracting the one-dimensional features of the text,neglecting its multidimensional semantic features.Digging deeply into the multidimensional semantic features of the text can improve the recommendations.To help achieve this goal,a probabilistic matrix factorization model based on multidimensional semantic representation learning is proposed in the present study.The model uses a capsule network to mine the multidimensional semantic features of the text,and then integrates it into the probabilistic matrix decomposition framework using the regularization method to reveal hidden features linking users and items.Experimental results show that the proposed model has higher prediction accuracy.
任开旭, 王玉龙, 刘同存, 李炜. 融合多维语义表示的概率矩阵分解模型[J]. 电子学报, 2019, 47(9): 1848-1854.
REN Kai-xu, WANG Yu-long, LIU Tong-cun, LI Wei. A Probabilistic Matrix Factorization Model Based on Multidimensional Semantic Representation Learning. Acta Electronica Sinica, 2019, 47(9): 1848-1854.
[1] Ma H,Yang H,Lyu M R,et al.Sorec:social recommendation using probabilistic matrix factorization[A].Proceedings of the 17th ACM Conference on Information and Knowledge Management[C].California:ACM,2008.931-940.
[2] 陈克寒,韩盼盼,吴健.基于用户聚类的异构社交网络推荐算法[J].计算机学报,2013,36(2):349-359. Chen Ke Han,Han Pan Pan,Wu Jian.User clustering based social network recommendation[J].Chinese Journal of Computers,2013,36(2):349-359.(in Chinese)
[3] Melville P,Mooney R J,Nagarajan R.Content-boosted collaborative filtering for improved recommendations[A].Proceeding Eighteenth National Conference on Artificial Intelligence[C].Menlo Park:AAAI,2002.187-192.
[4] 黄贤英,熊李媛,李沁东.基于改进协同过滤算法的个性化新闻推荐技术[J].四川大学学报(自然科学版),2018,55(01):49-55. Huang X Y,Xiong L Y,Qin-Dong LI.Personalized news recommendation technology based on improved collaborative filtering algorithm[J].Journal of Sichuan University,2018,55(01):49-55.(in Chinese)
[5] Blei D M,Ng A Y,Jordan M I.Latent dirichletallocation[J].Journal of Machine Learning Research,2003,3(Jan):993-1022.
[6] Hsieh C K,Yang L,Cui Y,et al.Collaborative metric learning[A].Proceedings of the 26th International Conference on World Wide Web[C].Republic and Canton of Geneva:IW3C2,2017.193-201.
[7] Vincent P,Larochelle H,Lajoie I,et al.Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(Dec):3371-3408.
[8] Kim D,Park C,Oh J,et al.Convolutional matrix factorization for document context-aware recommendation[A].Proceedings of the 10th ACM Conference on Recommender Systems[C].Boston:ACM,2016.233-240.
[9] Bansal T,Belanger D,McCallum A.Ask the gru:Multi-task learning for deep text recommendations[A].Proceedings of the 10th ACM Conference on Recommender Systems[C].Boston:ACM,2016.107-114.
[10] Li Z,Tang J.Weakly supervised deep matrix factorization for social image understanding[J].IEEE Transactions on Image Processing,2017,26(1):276-288.
[11] Li Z,Tang J,Mei T.Deep collaborative embedding for social image understanding[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,1(4):1-7.
[12] Liao J,Liu T,Liu M,et al.Multi-context integrated deep neural network model for next location prediction[J].IEEE Access,2018,(6):21980-21990.
[13] Sabour S,Frosst N,Hinton G E.Dynamic routing between capsules[A].Advances in Neural Information Processing Systems[C].California:NIPS,2017.3856-3866.
[14] Mnih A,Salakhutdinov R R.Probabilistic matrix factorization[A].Advances in Neural Information Processing Systems[C].California:NIPS,2008.1257-1264.
[15] Schein A I,Popescul A,Ungar L H,et al.Methods and metrics for cold-start recommendations[A].Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval[C].Tampere:ACM,2002.253-260.
[16] Zhou Y,Wilkinson D,Schreiber R,et al.Large-scale parallel collaborative filtering for the netflix prize[A].International Conference on Algorithmic Applications in Management[C].Shanghai:AAIM,2008.337-348.
[17] Wang C,Blei D M.Collaborative topic modeling for recommending scientific articles[A].Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].San Diego:ACM,2011.448-456.
[18] Wang H,Wang N,Yeung D Y.Collaborative deep learning for recommender systems[A].Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].Sydney:ACM,2015.1235-1244.