Open Access Open Access  Restricted Access Subscription or Fee Access

New Approach for Service Discovery and Prediction Based on Intentional Perspective and Recommendation


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v10i12.8091

Abstract


The goal of service selection issue is to generate suitable service to the customer. The selection was addressed by many approaches to face into the riches and the variety of services available online. Currently the users cannot find what they want easily, so to overcome this challenge a recommender system is needed. This system is widely applied in many fields such as commercial sites, social networks and service oriented architecture. It allows guiding of the user in their navigation, based on their previous choices or on its neighbors. Despite the advantages of such systems there are always limitations. We aim through this paper to tilt the angle of recommendation by intermixing between user intention and recommendation, to improve the selection issue.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords


Intention; Recommendation; Profile; Collaborative Filtering; Service Prediction

Full Text:

PDF


References


Melville, P., Sindhwani, V.: Recommender systems. Encyclopedia of machine learning 1, 829–838 (2010).
http://dx.doi.org/10.1007/978-0-387-30164-8_705

Sneha, Y., Thampi, N., Mahadevan, G., A Comparative and Performance Analysis Of Similarity Metrics In Recommneder System Based On Hadoop Framework, (2014) International Journal on Information Technology (IREIT), 2 (3), pp. 87-94.

G. Karypis, “Evaluation of item-based top-N recommendation algorithms,” in Proceedings of the International Conference on Information and Knowledge Management (CIKM ’01).
http://dx.doi.org/10.1145/502585.502627

Engonopoulos, N., M. Villalba, I. Titov, and A. Koller (2013). Predicting the resolution of referring expressions from user behavior. In Proceedings of EMLNP, Seattle, Washington, USA, pp. 1354– 1359. Association for Computational Linguistics.

J. Sobecki, Implementations of Web-based Recommender Systems Using Hybrid Methods, (2006) International Journal of Computer Science, Vol. 3 Issue 3, pp 52 - 64.

Ardissono, L., Goy,A., Petrone, G., Segnan, M., Torasso, P.: Tailoring the Recommendation of Tourist Information to Heterogeneous User Groups. In S. Reich, M. Tzagarakis, P. De Bra (eds.), Hypermedia: Openness, Structural Awareness, and Adaptivity, International Workshops OHS-7, SC-3, and AH-3. Lecture Notes in Computer Science 2266, Springer Verlag, Berlin (2002) 280-295.
http://dx.doi.org/10.1007/3-540-45844-1_26

Web service architecture, http://www.w3.org/tr/ws/-arch/,W3C, Working Notes 2003/2004.

K. aljoumaa, S, Assar and C. Souveyet, "Reformulating User's Queries for intentional services discovery using an ontology-based appraoch,""4 th IFIP Int,Conf on New Technologies, Mobility and Security (NTMS), Paris, France, app, 1-4,2011".
http://dx.doi.org/10.1109/ntms.2011.5721075

Aggarwal, C. C. 2011. An introduction to social network data analytics. In Social Network Data Analytics. Edited by C. C. Aggarwal. Springer: New York, pp. 1–15.
http://dx.doi.org/10.1007/978-1-4419-8462-3_1

Bonino da Silva Santos, L.O., Guizzardi, G., Pires, L.F. and Van Sinderen, M. From User Goals to Service Discovery and Composition, ER Workshops, pp. 265-274, 2009.
http://dx.doi.org/10.1007/978-3-642-04947-7_32

Mecella M., Presicce F.P., Pernici B., « Modeling e-service orchestration through Petri nets », In: TES’02, Hong Kong, China, 2002, p. 38–47.
http://dx.doi.org/10.1007/3-540-46121-3_6

C. Rolland, N. Prakash. Bridging the gap between Organizational needs and ERP functionality. Requirements Engineering Journal, Springer, 2000, pp.1.
http://dx.doi.org/10.1007/pl00010350

Alaoui Sara, Younes El Bouzekri El Idrissi, Rachida Ajhoun. Building rich user profile based on intentional perspective. The International Conference on Advanced Wireless, Information, and Communication Technologies (AWICT 2015).
http://dx.doi.org/10.1016/j.procs.2015.12.002

Daniar Asanov, 2011. Algorithms and Methods in Recommender Systems. Berlin Institute of Technology, Berlin, Germany.

Dietmar Jannach, ‎Markus Zanker, ‎Alexander Felfernig – Book:2010 Recommender Systems: An Introduction.
http://dx.doi.org/10.1017/cbo9780511763113

J. Lee, M. Sun, and G. Lebanon, “A Comparative Study of Collaborative Filtering Algorithms,” [Online] arXiv: 1205.3193, 2012.

Luo, P., Li, Y., Wu, C., A New Similarity Measure of Interval-Valued Intuitionistic Fuzzy Sets and its Application in Commodity Recommendation, (2013) International Journal on Information Technology (IREIT), 1 (3), pp. 186-192.

R. Jin, L. Si, and C. Zhai, “A study of mixture models for collaborative filtering,” Inf. Retr., vol. 9, no. 3, pp. 357–382, 2006.
http://dx.doi.org/10.1007/s10791-006-4651-1

Basu, C., Hirsh, H., and Cohen, W. (1998). Recommendation as Classification: Using Social and Content-based Information in Recommendation. In Recommender System Workshop ’98. pp. 11-15.

Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43-52.

Ungar, L. H., and Foster, D. P. (1998) Clustering Methods for Collaborative Filtering. In Workshop on Recommender Systems at the 15th National Conference on Artificial Intelligence.

Schein, A. I., A. Popescul, L. H. Ungar, and D. M. Pennock. Methods and metrics for cold-start recommendations. In Proc. of the 25th Annual Intl. ACM SIGIR Conf., 2002.
http://dx.doi.org/10.1145/564376.564421

Zan Huang , Hsinchun Chen , Daniel Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, ACM Transactions on Information Systems (TOIS), v.22 n.1, p.116-142, January2004.
http://dx.doi.org/10.1145/963770.963775


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize