Generic placeholder image

Recent Patents on Computer Science

Editor-in-Chief

ISSN (Print): 2213-2759
ISSN (Online): 1874-4796

Research Article

Location-Based Collaborative Filtering for Web Service Recommendation

Author(s): Mareeswari Venkatachalaappaswamy*, Vijayan Ramaraj and Saranya Ravichandran

Volume 12, Issue 1, 2019

Page: [34 - 40] Pages: 7

DOI: 10.2174/2213275911666181025130059

Price: $65

Abstract

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list.

Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services.

Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users.

Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources.

Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.

Keywords: Collaborative filtering, quality of service, location-based, web services, prediction, autonomous system number, KNN, PCC.

Graphical Abstract
[1]
Z. Yilei, Z. Zheng, and M.R. Lyu, "Exploring latent features for memory-based QoS prediction in cloud computing", 30th IEEE Symposium on Reliable Distributed Systems (SRDS), 2011pp. 1-10
[2]
Z. Zibin, Y. Zhang, and M.R. Lyu, "Distributed qos evaluation for real-world web services", In IEEE International Conference on Web Services, 2010pp. 83-90
[3]
"G. Amirag, A. Batra, R. Khurana and M. M. Tripathi, "Cognitive learning recommendation system in Indian context",", 5th National Conference on E-Learning & E-Learning Technologies (ELELTECH) 2017,. pp. 1-6, 2017.
[4]
"B. Gonsalves and V. Patil, “LoQoS location and QoS sensitive web service recommender”, In", International Conference on Innovations in Information, Embedded and Communication Systems. (ICIIECS), pp. 1-4, 2017.
[5]
J. Liu, M. Tang, Z. Zheng, X.F. Liu, and S. Lyu, "Location-aware and personalized collaborative filtering for web service recommendation", IEEE Trans. Serv. Comput., vol. 9, pp. 686-699, 2016.
[6]
V. Mareeswari, and E. Sathiyamoorthy, "Loc PSO rank-prediction of ranking of web services using location-based clustering and PSO algorithm", Int. J. Web Serv. Res., vol. 15, pp. 38-60, 2018.
[7]
"C. Bei-Bei, "Design and implementation of movie recommendation system based on KNN collaborative filtering algorithm", In", ITM Web of Conferences,. Vol. 12, pp. 04008, 2017
[8]
V. Mareeswari, R. Saranya, R. Mahalakshmi, and E. Preethi, "Prediction of diabetes using data mining techniques", Res. J. Pharm. Technol, vol. 10, pp. 1098-1104, 2017.
[9]
A.O. Omondi, and A.W. Mbugua, "An Application of association rule learning in recommender systems for e-Commerce and its effect on marketing", Pan African Conference on Science, Computing and Telecommunications (PACT), 2017
[10]
"P. Suhasini and B. Joshi, "Online book recommendation system by using collaborative filtering and association mining", In", IEEE International Conference on Computational Intelligence and Computing Research. (ICCIC), 2015, pp. 1-4, 2015
[11]
W. Jian, J. He, K. Chen, Y. Zhou, and Z. Tang, "Collaborative filtering and deep learning based recommendation system for cold start items", Expert Syst. Appl., vol. 69, pp. 29-39, 2017.
[12]
A. Taner, E. Köksal, and Z. Bozkus, "Comparison of collaborative filtering algorithms with various similarity measures for movie recommendation", Intl. J. Comput. Sci. Eng. App IJCSEA., vol. 6, pp. 1-20, 2016.
[13]
Z. Zibin, H. Ma, M.R. Lyu, and I. King, "QoS-aware web service recommendation by collaborative filtering", IEEE Trans. Serv. Comput., vol. 4, pp. 140-152, 2011.
[14]
"J. Yang, Y. Li and W. Cheng, “An improved neighbor-correlationextended- Kalman-filter fusion method for indoor navigation”,", Intl. J. Distrib. Sensor Netw., Vol. 13, 2017, Available from:.https://doi.org/10.1177/1550147717711651 [Accessed on: September 28, 2018].
[15]
Z. Zheng, Y. Zhang, and M.R. Lyu, "Investigating QoS of real- world web services", IEEE Trans. Serv. Comput., vol. 7, pp. 32-39, 2014.
[16]
A. Mucherino, P.J. Papajorgji, and P.M. Pardalos, k-Nearest Neighbor Classification.In Data Mining in Agriculture. Springer Optimization and Its Applications., vol. 34. Springer: New York, 2009.
[17]
"J. Liu, Y. Jiang, X. Liu and M. Tang, "An Effective Web Service Recommendation Method Based on Personalized Collaborative Filtering,"", IEEE International Conference on Web Services. (ICWS), Washington, DC USA, 2011, pp. 211-218.
[18]
R.J. Hyndman, and A.B. Koehler, "Another look at measures of forecast accuracy", Int. J. Forecast., vol. 22, pp. 679-688, 2006.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy