融合标签的实值条件受限波尔兹曼机推荐算法
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摘要
针对推荐算法中数据的稀疏性难题,把用户标签融合至实值条件受限玻尔兹曼机(real-valued conditional restricted Boltzmann machine,R_CRBM)模型,利用R_CRBM强大的拟合任意离散分布的能力,预测出用户对未交互商品的评分缺失值。具体来说,首先提出显层单元为实值的R_CRBM模型,接着运用文本分类中的TF-IDF算法预测出用户对所应用过的标签的喜爱度,与标签基因数据相乘得到用户对商品的预测评分,融合至用户历史评分数据中。R_CRBM条件层在原有评分/未评分{0,1}向量中,融入用户标签/未标签{0,1}向量。通过真实数据集进行对比分析,实验结果表明提出的方法在一定程度上提升了推荐的准确性。
To solve data sparseness problem of recommendation algorithm, this paper fuses the tag to the conditional restricted Boltzmann machines model. It utilizes CRBM. s powerful ability to fit arbitrary discrete distribution to predict the missing scores for the user unevaluated products. Specifically, it proposes the real-valued conditional restricted Boltzmann machine model whose visible units are real value firstly. Then, the TF-IDF algorithm of text classification is used to predict the attitude of the user who applies the tags, which is multiplied by the tag-genome to get the user.s scores for products, which are integrated into the user history rating data. In the conditional layer,this paper incorporates user tagged/untagged {0, 1} vector in the original rated/unrated {0, 1} vector. Finally, the comparative experimental analysis about real-world dataset shows that the proposed method enhances the accuracy of recommendation.
To solve data sparseness problem of recommendation algorithm, this paper fuses the tag to the conditional restricted Boltzmann machines model. It utilizes CRBM. s powerful ability to fit arbitrary discrete distribution to predict the missing scores for the user unevaluated products. Specifically, it proposes the real-valued conditional restricted Boltzmann machine model whose visible units are real value firstly. Then, the TF-IDF algorithm of text classification is used to predict the attitude of the user who applies the tags, which is multiplied by the tag-genome to get the user.s scores for products, which are integrated into the user history rating data. In the conditional layer,this paper incorporates user tagged/untagged {0, 1} vector in the original rated/unrated {0, 1} vector. Finally, the comparative experimental analysis about real-world dataset shows that the proposed method enhances the accuracy of recommendation.
引文
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[21]Massa P,Avesani P.Controversial users demand local trust metrics:an experimental study on Epinions.com community[C]//Proceedings of the 20th National Conference on Artificial Intelligence,Pittsburgh,Jul 9-13,2005.Menlo Park:AAAI,2005:121-126.
[22]Massa P,Avesani P.Trust metrics on controversial users:balancing between tyranny of the majority and echo chambers[J].International Journal on Semantic Web&Information Systems,2007,3(1):39-64.
[4]翁小兰,王志坚.协同过滤推荐算法研究进展[J].计算机工程与应用,2018,54(1):25-31.
[5]邸佳奇,王霓虹.一种基于GridGIS的增量式协同过滤算法[J].计算机科学,2013,40(12):219-222.
[7]王梦恬,魏晶晶,廖祥文,等.融合评论标签的个性化推荐算法[J].计算机科学与探索,2016,10(10):1429-1438.
[8]文俊浩,袁培雷,曾骏,等.基于标签主题的协同过滤推荐算法研究[J].计算机工程,2017,43(1):247-252.
[9]曾安,徐小强.基于好友关系和标签的混合协同过滤算法[J].计算机科学,2017,44(8):246-251.
[18]何洁月,马贝.利用社交关系的实值条件受限玻尔兹曼机协同过滤推荐算法[J].计算机学报,2016,39(1):183-195.
[2]Jannach D,Resnick P,Tuzhilin A,et al.Recommender systemsbeyond matrix completion[J].Communications of the ACM,2016,59(11):94-102.
[3]Marlin B.Collaborative filtering:a machine learning perspective[D].Toronto:University of Toronto,2004.
[4]Weng X L,Wang Z J.Research process of collaborative filtering recommendation algorithm[J].Computer Engineering and Applications,2018,54(1):25-31.
[5]Di J Q,Wang N H.Incremental collaborative filtering algorithm based on Grid GIS[J].Computer Science,2013,40(12):219-222.
[6]Hu Y F,Koren Y,Volinsky C.Collaborative filtering for implicit feedback datasets[C]//Proceedings of the 8th IEEEInternational Conference on Data Mining,Pisa,Dec 15-19,2008.Washington:IEEE Computer Society,2008:263-272.
[7]Wang M T,Wei J J,Liao X W,et al.Personalized recommendation algorithm fusing comment tag[J].Journal of Frontiers of Computer Science and Technology,2016,10(10):1429-1438.
[8]Wen J H,Yuan P L,Zeng J,et al.Research on collaborative filtering recommendation algorithm based on topic of tags[J].Computer Engineering,2017,43(1):247-252.
[9]Zeng A,Xu X Q,Computer S O.Hybrid collaborative filtering recommendation algorithm based on friendships and tag[J].Computer Science,2017,44(8):246-251.
[10]Bai X,Chen F,Zhan S B.A study on sentiment computing and classification of Sina weibo with word2vec[C]//Proceedings of the International Congress on Big Data,Anchorage,Jun 27-Jul 2,2014.Washington:IEEE Computer Society,2014:358-363.
[11]Zhou X,He J,Huang G Y,et al.SVD-based incremental approaches for recommender systems[J].Journal of Computer&System Sciences,2015,81(4):717-733.
[12]Funk S.Netflix update:try this at home[EB/OL].(2006-12-11)[2017-09-15].http//sifter.org/~simon/journal/20061211.html.
[13]Zhang S,Wang W H,Ford J,et al.Learning from incomplete ratings using non-negative matrix factorization[C]//Proceedings of the 6th SIAM International Conference on Data Mining,Bethesda,Apr 20-22,2006.Philadelphia:SIAM,2006:549-553.
[14]Salakhutdinov R,Mnih A.Probabilistic matrix factorization[C]//Proceedings of the 21st Annual Conference on Neural Information Processing Systems,Vancouver,Dec 3-6,2007.Red Hook:Curran Associates,2008:1257-1264.
[15]Zhang S,Yao L N,Sun A X.Deep learning based recommender system:a survey and new perspectives[J].ar Xiv:1707.07435,2017.
[16]Salakhutdinov R,Mnih A,Hinton G E.Restricted Boltzmann machines for collaborative filtering[C]//Proceedings of the 24th International Conference on Machine Learning,Corvallis,Jun 20-24,2007.New York:ACM,2007:791-798.
[17]Georgiev K,Nakov P.A non-IID framework for collaborative filtering with restricted Boltzmann machines[C]//Proceedings of the 30th International Conference on Machine Learning,Atlanta,Jun 16-Jun 21,2013:1148-1156.
[18]He J Y,Ma B.Based on real-valued conditional restricted Boltzmann machine and social network for collaborative filtering[J].Chinese Journal of Computers,2016,39(1):183-195.
[19]Vig J,Sen S,Riedl J.The tag genome:encoding community knowledge to support novel interaction[J].ACM Transactions on Interactive Intelligent Systems,2012,2(3):13.
[20]Ji N N,Zhang J S,Zhang C X,et al.Enhancing performance of restricted Boltzmann machines via log-sum regularization[J].Knowledge-Based Systems,2014,63(1):82-96.
[21]Massa P,Avesani P.Controversial users demand local trust metrics:an experimental study on Epinions.com community[C]//Proceedings of the 20th National Conference on Artificial Intelligence,Pittsburgh,Jul 9-13,2005.Menlo Park:AAAI,2005:121-126.
[22]Massa P,Avesani P.Trust metrics on controversial users:balancing between tyranny of the majority and echo chambers[J].International Journal on Semantic Web&Information Systems,2007,3(1):39-64.
[4]翁小兰,王志坚.协同过滤推荐算法研究进展[J].计算机工程与应用,2018,54(1):25-31.
[5]邸佳奇,王霓虹.一种基于GridGIS的增量式协同过滤算法[J].计算机科学,2013,40(12):219-222.
[7]王梦恬,魏晶晶,廖祥文,等.融合评论标签的个性化推荐算法[J].计算机科学与探索,2016,10(10):1429-1438.
[8]文俊浩,袁培雷,曾骏,等.基于标签主题的协同过滤推荐算法研究[J].计算机工程,2017,43(1):247-252.
[9]曾安,徐小强.基于好友关系和标签的混合协同过滤算法[J].计算机科学,2017,44(8):246-251.
[18]何洁月,马贝.利用社交关系的实值条件受限玻尔兹曼机协同过滤推荐算法[J].计算机学报,2016,39(1):183-195.