信任关系辅助的隐反馈Web服务推荐研究
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摘要
针对Web服务推荐现有技术缺乏显式打分数据缺点,提出使用隐反馈知识进行推荐的方法.该方法首先构造一个伪评分生成器,将用户隐反馈知识映射成为显式打分.基于矩阵因子分解模型,将信任知识引入服务推荐过程,建立一种融合社交信任信息的服务推荐模型,有效提高了服务推荐性能.实验表明,本文提出的基于隐反馈的服务推荐方法预测性能优于最近邻方法和SVD++方法;同SVD++方法的性能对比实验表明,引入信任知识能够进一步提高服务推荐的性能,具有较好的实际应用价值.
A new Web service recommendation method based on implicit feedback knowledge is proposed to avoid the flaws of current web service recommendation technology lacking of explicit rating data.The proposed method maps the users' implicit feedback into pseudo ratings by apseudo rating generator.Meanwhile,based on the matrix factorization model,the trust knowledge is integrated with web service recommendation,and then the Web service recommendation model with social trust information is established,so as to significantly improve the efficiency of service recommendation.Experiments results show that predict performance of the proposed solution outperforms the nearest neighbor and SVD++ method.Contrast experiments with SVD++ method also show that the overall performance of service recommendation can be further improved by introducing trust knowledge.The Web service recommendation method based on implicit feedback knowledge is of a good practical value.
A new Web service recommendation method based on implicit feedback knowledge is proposed to avoid the flaws of current web service recommendation technology lacking of explicit rating data.The proposed method maps the users' implicit feedback into pseudo ratings by apseudo rating generator.Meanwhile,based on the matrix factorization model,the trust knowledge is integrated with web service recommendation,and then the Web service recommendation model with social trust information is established,so as to significantly improve the efficiency of service recommendation.Experiments results show that predict performance of the proposed solution outperforms the nearest neighbor and SVD++ method.Contrast experiments with SVD++ method also show that the overall performance of service recommendation can be further improved by introducing trust knowledge.The Web service recommendation method based on implicit feedback knowledge is of a good practical value.
引文
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[2]SHAO L,ZHANG J,WEI Y,et al.Personalized qos prediction forweb services via collaborative filtering[C]//Proceedings of ICWS’07.Washington D C:IEEE Computer Society,2007:439-446.
[3]ZHENG Z,MA H,LYU M R,et al.Wsrec:A collaborative filtering based web service recommender system[C]//Proceedings of ICWS’09.Washington D C:IEEE Computer Society,2009:437-444.
[4]ZHAGN Q,DING C,CHI C H.Collaborative filtering based service ranking using invocation histories[C]//Proceedings of ICWS’11.Washington D C:IEEE Computer Society,2011:195-202.
[5]KANG G,LIU J,TANG M,et al.AWSR:Active web service recommendation based on usage history[C]//Proceedings of ICWS’12.Washington D C:IEEE Computer Society,2012:186-193.
[6]AMIN A,COLMAN A,GRUNSKE L.An approach to forecasting QoS attributes of web services based on ARIMA and GARCH models[C]//Proceedings of ICWS’12.Washington D C:IEEE Computer Society,2012:74-81.
[7]ZHANG X,HE K Q,WANG J,et al.Web service recommendation based on watchlist via temporal and tag preference fusion[C]//Proceedings of ICWS’14.Washington D C:IEEE Computer Society,2014:281-288.
[8]TIAN G,WANG J,HE K Q,et al.Time-aware Web service recommendations using implicit feedback[C]//Proceedings of ICWS’14.Washington D C:IEEE Computer Society,2014:273-280.
[9]BAO Y,FANG H,ZHANG J.Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation[C]//Proceedings of the 28th AAAI Conference on Artificial Intelligence.Washington D C:AAAI,2014:350-356.
[10]YANG B,LEI Y,LIU D,et al.Social collaborative filtering by trust[C]//Proceedings of the TwentyThird International Joint Conference on Artificial Intelligence.Washington D C:AAAI,2013:2747-2753.
[11]HU Y,KOREN Y,VOLINSKY C.Collaborative filtering for implicit feedback datasets[C]//Proceedings of ICDM’08.Washington D C:IEEE Computer Society,2008:263-272.