QoS Prediction forWeb Services Based on Similarity-Aware Slope One Collaborative Filtering

Web services have become the primary source for constructing software system over Internet. The quality of whole system greatly dependents on the QoS of single Web service, so QoS information is an important indicator for service selection. In reality, QoSs of some Web services may be unavailable for users. How to predicate the missing QoS value of Web service through fully using the existing information is a difficult problem. This paper attempts to settle this difficulty through combining Pearson similarity and Slope One method together for QoS prediction. In the paper, we adopt the Pearson similarity between two services as the weight of their deviation. Meanwhile, some strategies like weight adjustment and SPCbased smoothing are also utilized for reducing prediction error. In order to evaluate the validity of our algorithm (i.e., similarity-aware Slope One algorithm, SASO), comparative experiments are performed on the real-world data set. The results show that SASO algorithm exhibits better prediction precision than both basic Slope One and the well-known WsRec algorithm in most cases. Meanwhile, our approach has the strong ability of reducing the impact of noise data.

[1]  DeJia Zhang An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing , 2009, 2009 Second International Symposium on Electronic Commerce and Security.

[2]  Zibin Zheng,et al.  QoS-Aware Web Service Recommendation by Collaborative Filtering , 2011, IEEE Transactions on Services Computing.

[3]  Kun Zhang,et al.  A New QoS Prediction Approach Based on User Clustering and Regression Algorithms , 2011, 2011 IEEE International Conference on Web Services.

[4]  Daniel A. Menascé,et al.  QoS Issues in Web Services , 2002, IEEE Internet Comput..

[5]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[6]  Sachin Garg,et al.  Response prediction using collaborative filtering with hierarchies and side-information , 2011, KDD.

[7]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[8]  Zibin Zheng,et al.  NRCF: A Novel Collaborative Filtering Method for Service Recommendation , 2011, 2011 IEEE International Conference on Web Services.

[9]  Junfeng Zhao,et al.  Personalized QoS Prediction forWeb Services via Collaborative Filtering , 2007, IEEE International Conference on Web Services (ICWS 2007).

[10]  Mingdong Tang,et al.  An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering , 2011, 2011 IEEE International Conference on Web Services.

[11]  Pu Wang,et al.  A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering , 2009, 2009 International Conference on Industrial and Information Systems.

[12]  Daniel Lemire,et al.  Slope One Predictors for Online Rating-Based Collaborative Filtering , 2007, SDM.

[13]  Feng Zhou,et al.  Research on Trust Prediction Model for Selecting Web Services Based on Multiple Decision Factors , 2011, Int. J. Softw. Eng. Knowl. Eng..

[14]  Jiao Wang,et al.  A Slope One Collaborative Filtering Recommendation Algorithm Using Uncertain Neighbors Optimizing , 2011, WAIM Workshops.

[15]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[16]  Min Chen,et al.  Personalized Context-Aware QoS Prediction for Web Services Based on Collaborative Filtering , 2010, ADMA.