基于图过滤的快速密度聚类双层网络推荐算法
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摘要
信息过载问题使得推荐系统迅速发展并广泛应用,同时也出现不法商家将虚假消费记录定量地输入到系统数据库从而改变推荐系统的推荐结果以获利.因此,本文围绕3个问题展开,即:为了提高推荐系统对虚假评论的鉴别能力,首先需要准确标注虚假评论的类标,如何能获取大量准确标定的虚假评论信息;如何有效过滤虚假评论从而提高推荐的可靠性;如何实现一种高效可靠的推荐系统.针对虚假评论信息难以准确标定,本文提出了一种基于文本生成式对抗网络的自动点评技术,依据历史评论文本自动生成虚假评论文本,并依据情感分析确定生成文本的对应评分;为了提高推荐系统对包含虚假信息数据的推荐效果,本文提出了一种基于图过滤的快速密度聚类双层网络推荐算法.该算法首先提出了一种能快速确定节点执行度阈值的基于图的过滤器,有效过滤数据内虚假信息,并设计了一种快速密度聚类双层网络推荐算法,提高推荐效果.将所提出的推荐算法应用到Yelp数据集上展开试验,验证本文提出的推荐方法的有效性.
The information overloading problem leads to wider application of recommender system. At the meantime,fake reviewers are quantitative input into the history review records by illegal business to affect the recommender to change for their benefits. Three research questions are addressed in our paper. In order to improve fake review filtering ability for recommenders, abundant of accurately labeled fake reviewers are necessary. How to collect large amount of accurately labeled fake reviewers? How to filter fake reviewers accurately and efficiently? How to design an efficient recommender?Since it's difficult to collect labeled fake reviewers, an automatic reviewer generator based on text generative adversarial nets is proposed. Reviewers labeled as fake can be generated based on historical reviewers and can be rated according to emotional analysis. In order to improve the recommendation effect of containing false information data, this paper proposes a double layered recommendation algorithm based on fast density clustering and graph-based filtering. Firstly, we design a graph-based filter that can quickly determine node execution thresholds to effectively filters the false information. And a recommender based on fast clustering is put forward, which is a density based clustering algorithm with cluster center self-determined, to implement accurate recommendation. At last, the proposed algorithm is applied to the Yelp data set to verify its effectiveness.
The information overloading problem leads to wider application of recommender system. At the meantime,fake reviewers are quantitative input into the history review records by illegal business to affect the recommender to change for their benefits. Three research questions are addressed in our paper. In order to improve fake review filtering ability for recommenders, abundant of accurately labeled fake reviewers are necessary. How to collect large amount of accurately labeled fake reviewers? How to filter fake reviewers accurately and efficiently? How to design an efficient recommender?Since it's difficult to collect labeled fake reviewers, an automatic reviewer generator based on text generative adversarial nets is proposed. Reviewers labeled as fake can be generated based on historical reviewers and can be rated according to emotional analysis. In order to improve the recommendation effect of containing false information data, this paper proposes a double layered recommendation algorithm based on fast density clustering and graph-based filtering. Firstly, we design a graph-based filter that can quickly determine node execution thresholds to effectively filters the false information. And a recommender based on fast clustering is put forward, which is a density based clustering algorithm with cluster center self-determined, to implement accurate recommendation. At last, the proposed algorithm is applied to the Yelp data set to verify its effectiveness.
引文
[1]SU X,KHOSHGOFTAAR T M.A survey of collaborative filtering techniques.Advances in Artificial Intelligence,2009,2009(12):102-120.
[2]ZHAO Z L,HUANG L,WANG C D,et al.Low-rank and sparse matrix completion for recommendation.International Conference on Neural Information Processing.Guangzhou,China:Springer,2017:3-13.
[3]LI Chunsheng,WANG Yaonan.New initialization method for cluster center.Control Theory&Applications,2010,27(10):1435-1440.(李春生,王耀南.聚类中心初始化的新方法.控制理论与应用,2010,27(10):1435-1440.)
[4]CHEN Jinyin,HE Huihao.Density-based clustering algorithm for numerical and categorical data with mixed distance measure methods.Control Theory&Applications,2015,32(8):993-1002.(陈晋音,何辉豪.基于密度和混合距离度量方法的混合属性数据聚类研究.控制理论与应用,2015,32(8):993-1002.)
[5]XUE G R,LIN C,YANG Q,et al.Scalable collaborative filtering using cluster-based smoothing.International ACM SIGIR Conference on Research and Development in Information Retrieval.Salvador,Brazil:ACM,2005:114-121.
[6]GUO G,ZHANG J,YORKE-SMITH N.Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems.Knowledge-Based Systems,2015,74(1):14-27.
[7]JINDAL N,LIU B.Opinion spam and analysis.International Conference on Web Search and Data Mining.California,USA:ACM,2008:219-230.
[8]GOODFELLOW I J,POUGETABADIE J,MIRZA M,et al.Generative adversarial networks.Advances in Neural Information Processing Systems,2014,3(2):2672-2680.
[9]ZHANG Y,GAN Z,CARIN L.Generating text via adversarial training.NIPS Workshop on Adversarial Training.Barcelona,Spain:NIP-S,2016:2101-2106..
[10]SALIMANS T,GOODFELLOW I,ZAREMBA W,et al.Improved techniques for training gans.Advances in Neural Information Processing Systems.Barcelona,Spain:NIPS,2016:2234-2242.
[11]JOSEPH K,TAN C H,CARLEY K M.Beyond local,categories and friends:clustering foursquare users with latent topics.Proceedings of the 2012 ACM Conference on Ubiquitous Computing.Pittsburgh,USA:ACM,2012:919-926.
[12]RANA C,JAIN S K.An evolutionary clustering algorithm based on temporal features for dynamic recommender systems.Swarm and Evolutionary Computation,2014,14(1):21-30.
[13]WANG Q,CAO W,LIU Y.A novel clustering based collaborative filtering recommendation system algorithm.Advanced Technologies,Embedded and Multimedia for Human-Centric Computing.Dordrecht:Springer,2014:673-680.
[14]PUNTHEERANURAK S,TSUJI H.A multi-clustering hybrid recommender system.The 7th IEEE International Conference on Computer and Information Technology.Fukushima,Japan:IEEE,2007:223-228.
[15]CONNOR M,HERLOCKER J.Clustering items for collaborative filtering.Proceedings of the ACM SIGIR Workshop on Recommender Systems.San Francisco,CA,USA:ACM,1999:64-68.
[16]LIM E P,NGUYEN V A,JINDAL N,et al.Detecting product review spammers using rating behaviors.Proceedings of the 19th ACM International Conference on Information and Knowledge Management.San Francisco,CA,USA:ACM,2010:939-948.
[17]LI F,HUANG M,YANG Y,et al.Learning to identify review spam.IJCAI Proceedings-International Joint Conference on Artificial Intelligence.New York,USA:ACM,2011:2488-2493.
[18]LIM E P,NGUYEN V A,JINDAL N,et al.Detecting product review spammers using rating behaviors.Proceedings of the 19th ACM International Conference on Information and Knowledge Management.San Francisco,CA,USA:ACM,2010:939-948.
[19]WANG G,XIE S,LIU B,et al.Review graph based online store review spammer detection.The 11th International Conference on Data Mining.New Orleans,LA,USA:IEEE,2011:1242-1247.
[20]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks.Science,2014,344(6191):1492-1496.
[21]ZHOU T,REN J,MEDO M,et al.Bipartite network projection and personal recommendation.Physical Review E,2007,76(4):046115.
[22]BENNETT J,LANNING S.The netflix prize.Proceedings of KDDCup and Workshop.San Jose,California,USA:ACM,2007:15-18.
[23]LIU B,FU Y,YAO Z,et al.Learning geographical preferences for point-of-interest recommendation.ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Chicago,USA:ACM,2013:1043-1051.
[2]ZHAO Z L,HUANG L,WANG C D,et al.Low-rank and sparse matrix completion for recommendation.International Conference on Neural Information Processing.Guangzhou,China:Springer,2017:3-13.
[3]LI Chunsheng,WANG Yaonan.New initialization method for cluster center.Control Theory&Applications,2010,27(10):1435-1440.(李春生,王耀南.聚类中心初始化的新方法.控制理论与应用,2010,27(10):1435-1440.)
[4]CHEN Jinyin,HE Huihao.Density-based clustering algorithm for numerical and categorical data with mixed distance measure methods.Control Theory&Applications,2015,32(8):993-1002.(陈晋音,何辉豪.基于密度和混合距离度量方法的混合属性数据聚类研究.控制理论与应用,2015,32(8):993-1002.)
[5]XUE G R,LIN C,YANG Q,et al.Scalable collaborative filtering using cluster-based smoothing.International ACM SIGIR Conference on Research and Development in Information Retrieval.Salvador,Brazil:ACM,2005:114-121.
[6]GUO G,ZHANG J,YORKE-SMITH N.Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems.Knowledge-Based Systems,2015,74(1):14-27.
[7]JINDAL N,LIU B.Opinion spam and analysis.International Conference on Web Search and Data Mining.California,USA:ACM,2008:219-230.
[8]GOODFELLOW I J,POUGETABADIE J,MIRZA M,et al.Generative adversarial networks.Advances in Neural Information Processing Systems,2014,3(2):2672-2680.
[9]ZHANG Y,GAN Z,CARIN L.Generating text via adversarial training.NIPS Workshop on Adversarial Training.Barcelona,Spain:NIP-S,2016:2101-2106..
[10]SALIMANS T,GOODFELLOW I,ZAREMBA W,et al.Improved techniques for training gans.Advances in Neural Information Processing Systems.Barcelona,Spain:NIPS,2016:2234-2242.
[11]JOSEPH K,TAN C H,CARLEY K M.Beyond local,categories and friends:clustering foursquare users with latent topics.Proceedings of the 2012 ACM Conference on Ubiquitous Computing.Pittsburgh,USA:ACM,2012:919-926.
[12]RANA C,JAIN S K.An evolutionary clustering algorithm based on temporal features for dynamic recommender systems.Swarm and Evolutionary Computation,2014,14(1):21-30.
[13]WANG Q,CAO W,LIU Y.A novel clustering based collaborative filtering recommendation system algorithm.Advanced Technologies,Embedded and Multimedia for Human-Centric Computing.Dordrecht:Springer,2014:673-680.
[14]PUNTHEERANURAK S,TSUJI H.A multi-clustering hybrid recommender system.The 7th IEEE International Conference on Computer and Information Technology.Fukushima,Japan:IEEE,2007:223-228.
[15]CONNOR M,HERLOCKER J.Clustering items for collaborative filtering.Proceedings of the ACM SIGIR Workshop on Recommender Systems.San Francisco,CA,USA:ACM,1999:64-68.
[16]LIM E P,NGUYEN V A,JINDAL N,et al.Detecting product review spammers using rating behaviors.Proceedings of the 19th ACM International Conference on Information and Knowledge Management.San Francisco,CA,USA:ACM,2010:939-948.
[17]LI F,HUANG M,YANG Y,et al.Learning to identify review spam.IJCAI Proceedings-International Joint Conference on Artificial Intelligence.New York,USA:ACM,2011:2488-2493.
[18]LIM E P,NGUYEN V A,JINDAL N,et al.Detecting product review spammers using rating behaviors.Proceedings of the 19th ACM International Conference on Information and Knowledge Management.San Francisco,CA,USA:ACM,2010:939-948.
[19]WANG G,XIE S,LIU B,et al.Review graph based online store review spammer detection.The 11th International Conference on Data Mining.New Orleans,LA,USA:IEEE,2011:1242-1247.
[20]RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks.Science,2014,344(6191):1492-1496.
[21]ZHOU T,REN J,MEDO M,et al.Bipartite network projection and personal recommendation.Physical Review E,2007,76(4):046115.
[22]BENNETT J,LANNING S.The netflix prize.Proceedings of KDDCup and Workshop.San Jose,California,USA:ACM,2007:15-18.
[23]LIU B,FU Y,YAO Z,et al.Learning geographical preferences for point-of-interest recommendation.ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Chicago,USA:ACM,2013:1043-1051.