Collaborative Filtering Based Service Ranking Using Invocation Histories

Collaborative filtering based recommender systems are very successful on dealing with the information overload problem and providing personalized recommendations to users. When more and more web services are published online, this technique can also help recommend and select services which satisfy users' particular Quality of Service (QoS) requirements and preferences. In this paper, we propose a novel collaborative filtering based service ranking mechanism, in which the invocation and query histories are used to infer the user behavior, and user similarity is calculated based on similar invocations and queries. To overcome some of the inherent problems with the collaborative filtering systems such as the cold start and data sparsity problem, the final ranking score is a combination of the QoS-based matching score and the collaborative filtering based score. The experiment using a simulated dataset proves the effectiveness of the algorithm.

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

[2]  Kecheng Liu,et al.  Personalized Web Service Ranking via User Group Combining Association Rule , 2009, 2009 IEEE International Conference on Web Services.

[3]  Eyhab Al-Masri,et al.  QoS-based Discovery and Ranking of Web Services , 2007, 2007 16th International Conference on Computer Communications and Networks.

[4]  Javed A. Aslam,et al.  Models for metasearch , 2001, SIGIR '01.

[5]  H. Abdi.  The Kendall Rank Correlation Coefficient , 2007 .

[6]  Anne H. H. Ngu,et al.  QoS computation and policing in dynamic web service selection , 2004, WWW Alt. '04.

[7]  Antonio Ruiz Cortés,et al.  Improving the Automatic Procurement of Web Services Using Constraint Programming , 2005, Int. J. Cooperative Inf. Syst..

[8]  Anupriya Ankolekar,et al.  Preference-based selection of highly configurable web services , 2007, WWW '07.

[9]  Athman Bouguettaya,et al.  Computing Service Skyline from Uncertain QoWS , 2010, IEEE Transactions on Services Computing.

[10]  Feng Liu,et al.  A Semantic QoS-Aware Discovery Framework for Web Services , 2008, 2008 IEEE International Conference on Web Services.

[11]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[12]  Bamshad Mobasher,et al.  Data Mining for Web Personalization , 2007, The Adaptive Web.

[13]  Hei-Chia Wang,et al.  Combining subjective and objective QoS factors for personalized web service selection , 2007, Expert Syst. Appl..

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

[15]  Dan Frankowski,et al.  Collaborative Filtering Recommender Systems , 2007, The Adaptive Web.

[16]  Caroline Herssens,et al.  Dealing with Quality Tradeoffs during Service Selection , 2008, 2008 International Conference on Autonomic Computing.

[17]  Yue Tan,et al.  QoS Browsing for Web Service Selection , 2009, ICSOC/ServiceWave.

[18]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[19]  Jun Yan,et al.  Towards QoS-Based Web Services Discovery , 2008, ICSOC Workshops.

[20]  Eric Brill,et al.  Improving web search ranking by incorporating user behavior information , 2006, SIGIR.

[21]  Susan T. Dumais,et al.  Improving Web Search Ranking by Incorporating User Behavior Information , 2019, SIGIR Forum.

[22]  Enrico Blanzieri,et al.  Improving Web Service Discovery with Usage Data , 2007, IEEE Software.

[23]  Vassilika Vouton,et al.  Mixed-Integer Programming for QoS-Based Web Service Matchmaking , 2009 .

[24]  Dimitrios Skoutas,et al.  Recommend me a Service: Personalized Semantic Web Service Matchmaking , 2009, LWA.

[25]  Hidekazu Tsuji,et al.  A new QoS ontology and its QoS-based ranking algorithm for Web services , 2009, Simul. Model. Pract. Theory.

[26]  Wei-Ying Ma,et al.  Optimizing web search using web click-through data , 2004, CIKM '04.

[27]  Daniel A. Menascé,et al.  Utility-based QoS Brokering in Service Oriented Architectures , 2007, IEEE International Conference on Web Services (ICWS 2007).

[28]  Verena Kantere,et al.  Top-k dominant web services under multi-criteria matching , 2009, EDBT '09.

[29]  T.V. Prabhakar,et al.  Dynamic selection of Web services with recommendation system , 2005, International Conference on Next Generation Web Services Practices (NWeSP'05).