AN EFFICIENT SIMILARITY-BASED MODEL FOR WEB SERVICE RECOMMENDATION

With the emergence of Cloud and Mobile Computing paradigms which adopt web services (WS) as a means of management, companies worldwide are actively deploying web services within their business environments. Consequently, designing effective Web service recommendation mechanisms is receiving more research attention. Traditional Neighborhood-based Collaborative Filtering (CF) models fail to capture the actual relationships between users and services due to data sparsity. In contrast, Random Walk (RW) algorithm, which is categorized as a sparsitytolerant recommendation approach, suffers from poor recommendation accuracy. In this paper, we first propose an Integrated-Model QoS-based Graph (IMQG), in which users and services represent nodes while weighted Quality of Service (QoS) magnitudes and User/Service similarity measurements serve as edges. Variants of Jaccard coefficient are used to separately compute these similarities. Then, Top-k Random Walk algorithm is applied to generate a final recommendation list. Further, we reduce the model by selecting only a subset of users to better guide RW algorithm. Comprehensive experiments are conducted on a real-world dataset where analysis of results shows high improvement in recommendation accuracy with more tolerance to data sparsity. Utilizing the improved model, a considerable reduction in computation time is achieved while maintaining the recommendation accuracy.

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

[2]  Daniel Dajun Zeng,et al.  A Random Walk Model for Item Recommendation in Social Tagging Systems , 2013, TMIS.

[3]  Yang Zhou,et al.  Ranking Services by Service Network Structure and Service Attributes , 2013, 2013 IEEE 20th International Conference on Web Services.

[4]  Abdullah Abdullah,et al.  An Integrated-Model QoS-Based Graph for Web Service Recommendation , 2015, 2015 IEEE International Conference on Web Services.

[5]  Thomas Hofmann,et al.  Latent semantic models for collaborative filtering , 2004, TOIS.

[6]  Lina Yao,et al.  Recommending Web Services via Combining Collaborative Filtering with Content-Based Features , 2013, 2013 IEEE 20th International Conference on Web Services.

[7]  Roberto Turrin,et al.  Performance of recommender algorithms on top-n recommendation tasks , 2010, RecSys '10.

[8]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[9]  Chih-Fong Tsai,et al.  Cluster ensembles in collaborative filtering recommendation , 2012, Appl. Soft Comput..

[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]  Xiaohui Hu,et al.  A Time-Aware and Data Sparsity Tolerant Approach for Web Service Recommendation , 2014, 2014 IEEE International Conference on Web Services.

[12]  Zibin Zheng,et al.  Web Service Recommendation via Exploiting Location and QoS Information , 2014, IEEE Transactions on Parallel and Distributed Systems.

[13]  John F. Canny,et al.  Collaborative filtering with privacy via factor analysis , 2002, SIGIR '02.

[14]  Luo Si,et al.  An automatic weighting scheme for collaborative filtering , 2004, SIGIR '04.

[15]  Zibin Zheng,et al.  WSRec: A Collaborative Filtering Based Web Service Recommender System , 2009, 2009 IEEE International Conference on Web Services.

[16]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.