A Novel Framework for Service Set Recommendation in Mashup Creation

With an overwhelming number of web services online, recommending services for automatic mashup creation greatly facilitates the composition process of developers. Various approaches have been proposed for the task. However, these approaches concentrate on improving the recommending accuracy of an individual service, which give rise to two problems: (1) Top-ranked services may be highly redundant with the same functionality, and (2) The cooperation relations among services are ignored. Therefore, we argue that services should be recommended not individually, but collectively. In this paper, we focus on the problem of recommending service sets instead of services. A service set contains a list of functionally distinct services that collectively match different aspects of functional requirements and are more inclined to compose together following mashup composition patterns. To this end, we propose a novel recommendation framework consisting of two stages: Service Set Generation Stage and Service Set Ranking Stage. We also perform an experimental evaluation on ProgrammableWeb dataset to demonstrate the effectiveness of our framework.

[1]  Hailong Sun,et al.  A Novel Approach for API Recommendation in Mashup Development , 2014, 2014 IEEE International Conference on Web Services.

[2]  Minglu Li,et al.  A Social-Aware Service Recommendation Approach for Mashup Creation , 2013, 2013 IEEE 20th International Conference on Web Services.

[3]  Jian Cao,et al.  Service Package Recommendation for Mashup Development Based on a Multi-level Relational Network , 2016, ICSOC.

[4]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

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

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

[7]  Zhaohui Wu,et al.  SMS: A Framework for Service Discovery by Incorporating Social Media Information , 2019, IEEE Transactions on Services Computing.

[8]  MengChu Zhou,et al.  Automatic Web Service Composition Based on Uncertainty Execution Effects , 2016, IEEE Transactions on Services Computing.

[9]  Zibin Zheng,et al.  Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization , 2013, IEEE Transactions on Services Computing.

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

[11]  Cheng Wu,et al.  Category-Aware API Clustering and Distributed Recommendation for Automatic Mashup Creation , 2015, IEEE Transactions on Services Computing.

[12]  Qi Yu,et al.  Aggregating Functionality, Use History, and Popularity of APIs to Recommend Mashup Creation , 2015, ICSOC.

[13]  Cheng Wu,et al.  SeCo-LDA: Mining Service Co-occurrence Topics for Recommendation , 2016, 2016 IEEE International Conference on Web Services (ICWS).

[14]  Hernán Astudillo,et al.  Simplifying mashup component selection with a combined similarity- and social-based technique , 2011, Mashups '11.

[15]  Ruoming Jin,et al.  A Hypergraph-based Method for Discovering Semantically Associated Itemsets , 2011, 2011 IEEE 11th International Conference on Data Mining.

[16]  François Fouss,et al.  Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation , 2007, IEEE Transactions on Knowledge and Data Engineering.