Integrating Tag, Topic, Co-Occurrence, and Popularity to Recommend Web APIs for Mashup Creation

With the rapid development of Web APIs, selection of the suitable Web APIs from the service repositories for users to build Mashup applications becomes more and more difficult. Even if the existing methods show significant improvements in Web API recommendation, it is still challenging to recommend similar, diverse, and relevant Web APIs with high accuracy. In this paper, we propose a novel Web API recommendation method, which integrates tag, topic, co-occurrence, and popularity factors to recommend Web APIs for Mashup creation. This method, firstly exploits the enriched tags and topics information of Mashups and Web APIs derived by the relational topic model to calculate the similarity between Web APIs and the similarity between Mashups. Secondly, it uses the invocation times and category information of Web APIs to derive their popularity. Thirdly, multi-dimensional information, such as similar Mashups, similar Web APIs, co-occurrence and popularity of Web APIs, are modeled by factorization machines to predict and recommend top-k similar, diverse, relevant Web APIs for a target Mashup. Finally, we conduct a set of experiments, and experimental results show that our approach achieves a significant improvement in terms of precision, recall, F-measure, compared with other existing methods.

[1]  Zibin Zheng,et al.  WTCluster: Utilizing Tags for Web Services Clustering , 2011, ICSOC.

[2]  Liang Chen,et al.  Joint Modeling Users, Services, Mashups, and Topics for Service Recommendation , 2016, 2016 IEEE International Conference on Web Services (ICWS).

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

[4]  Liang Chen,et al.  Manifold-Learning Based API Recommendation for Mashup Creation , 2015, 2015 IEEE International Conference on Web Services.

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

[6]  Arun Iyengar,et al.  Combining Quality of Service and Social Information for Ranking Services , 2009, ICSOC/ServiceWave.

[7]  David M. Blei,et al.  Relational Topic Models for Document Networks , 2009, AISTATS.

[8]  Mingdong Tang,et al.  Mashup Service Clustering Based on an Integration of Service Content and Network via Exploiting a Two-Level Topic Model , 2016, 2016 IEEE International Conference on Web Services (ICWS).

[9]  Mingdong Tang,et al.  Using Relational Topic Model and Factorization Machines to Recommend Web APIs for Mashup Creation , 2016, APSCC.

[10]  Zhaohui Wu,et al.  Collaborative Web Service QoS Prediction with Location-Based Regularization , 2012, 2012 IEEE 19th International Conference on Web Services.

[11]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

[12]  Jun Zhang,et al.  HyperService: Linking and Exploring Services on the Web , 2010, 2010 IEEE International Conference on Web Services.

[13]  Mingdong Tang,et al.  Three-Level Views of the Web Service Network: An Empirical Study Based on ProgrammableWeb , 2014, 2014 IEEE International Congress on Big Data.

[14]  Steffen Rendle,et al.  Factorization Machines with libFM , 2012, TIST.

[15]  Zibin Zheng,et al.  Location-Based Hierarchical Matrix Factorization for Web Service Recommendation , 2014, 2014 IEEE International Conference on Web Services.

[16]  Wei Sun,et al.  Towards Service Composition Based on Mashup , 2007, 2007 IEEE Congress on Services (Services 2007).

[17]  Lu Fang,et al.  Towards Automatic Tagging for Web Services , 2012, 2012 IEEE 19th International Conference on Web Services.

[18]  Zibin Zheng,et al.  Mashup Service Recommendation Based on Usage History and Service Network , 2013, Int. J. Web Serv. Res..

[19]  Lina Yao,et al.  Unified Collaborative and Content-Based Web Service Recommendation , 2015, IEEE Transactions on Services Computing.

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

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

[22]  Sana Sellami,et al.  WSTP: Web Services Tagging Platform , 2015, ICSOC.

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

[24]  Steffen Rendle,et al.  Learning recommender systems with adaptive regularization , 2012, WSDM '12.

[25]  Zibin Zheng,et al.  Mashup Service Recommendation Based on User Interest and Social Network , 2013, 2013 IEEE 20th International Conference on Web Services.

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