Weighted utility based recommender for e-procurement in handicraft communities
Pages 448 - 452
Abstract
In this paper, we would like to assess the positive impact of the recommendation process during the professional activities of business actors. We are interested specifically in the improvement of the economic life of the handicraft women from emerging countries. To this end, we introduce a utility based recommender dealing with the procurement opportunities. Actually, we proposed a utility function which takes into account the weighted preferences and expectations of final users. The system is evaluated based on the gain to obtain if the proposed recommendations are adopted in addition to the satisfaction level of the final users.
References
[1]
Akman, V., Surav, M. 1996. Steps toward formalizing context. AI Magazine. 17(3): 55--72, https://doi.org/10.1609/aimag.v17i3.1231
[2]
Aznoli, F., Navimipour, N. J. 2017. Cloud services recommendation: Reviewing the recent advances and suggesting the future research directions. J. Network and Computer Applications, 77: 73--86, https://doi.org/10.1016/j.jnca.2016.10.009
[3]
Burke, R. Hybrid web recommender systems. 2007. The Adaptive Web, Springer, Berlin, Heidelberg. 377--408, ttps://doi.org/10.1007/978-3-540-72079-9_12
[4]
Chinosi, M., Trombetta, A. 2012. BPMN: An introduction to the standard. Computer Standards & Interfaces. 34(1), 124--134, https://doi.org/10.1016/j.csi.2011.06.002
[5]
De Maio, C., Fenza, G., Gaeta, M., Loia, V., Orciuoli, F., Senatore, S. 2012. RSS-based e-learning recommendations exploiting fuzzy FCA for Knowledge Modeling. Appl. Soft Comput. 12(1): 113--124, https://doi.org/10.1016/j.asoc.2011.09.004
[6]
Dey, A. K., Salber, D., Abowd, G. D. 2002. A Conceptual Framework and a Toolkit for Supporting the Rapid Prototyping of Context-Aware Applications. Context-Aware Computing. A Special Triple Issue of Human-Computer Interaction. 16(2): 97--166, https://doi.org/10.1207/s15327051hci16234_02
[7]
Dhaouadi, R., Ben Miled, A., Ghédira, K. 2014. Ontology based Multi Agent System for Improved Procurement Process: Application for the Handicraft Domain. 18th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems. 251--260. Gdynia-Poland: Elseiver, https://doi.org/10.1016/j.procs.2014.08.105
[8]
Duen-Ren, L., Chin-Hui, L., Wang-Jung, L. 2009. A hybrid of sequential rules and collaborative filtering for product recommendation, Inf. Sci. 179 (20). 3505--3519, https://doi.org/10.1016/j.ins.2009.06.004
[9]
Encheva, S. B. 2016. Supplier Selection Based on Recommendations. CDVE. 86--89, https://doi.org/10.1007/978-3-319-46771-9_11
[10]
Giboney, J. S., Brown, S. A., Lowry, P. B., Nunamaker, J. F. 2015. User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit. Decision Support Systems. 72: 1--10, https: doi.org/10.1109/hicss.2012.624
[11]
Giovannucci, A., Rodríguez-Aguilar, J. A., Reyes, A., Noria, F. X., Cerquides, J. 2008. Enacting agent-based services for automated procurement. Eng. Appl. of AI, vol. 21, no. 2, 183--199, https://doi.org/10.1016/j.engappai.2007.04.006
[12]
Govindan, K., Shankar, M., Kannan, D. 2018. Supplier selection based on corporate social responsibility practice. International Journal of Production Economics. 353--379, https:doi.org/10.1016/j.ijpe.2016.09.003
[13]
Hill, W., Stead, L., Rosenstein, M., Furnas, G. 1995. Recommending and evaluating choices in a virtual community of use, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, CO, United States, 194--201. https://doi.org/10.1145 223904.223929
[14]
Krasnodebski, J., Dines, J. 2016. Considering Supplier Relations and Monetization in Designing Recommendation Systems. RecSys. 381--382, https://doi.org/10.1145/2959100.2959124
[15]
Lee, S., Choi, K., Suh, Y. 2013. A personalized trustworthy seller recommendation in an open market. Expert Syst. Appl, vol. 40, no. 4, 1352--1357, https://doi.org/10.1016/j.eswa.2012.08.054
[16]
Mahmood, T. and Ricci, F. 2009. Improving recommender systems with adaptive conversational strategies. 20th ACM conference on Hypertext and hypermedia. 73--8, https://doi.org/10.1145/1557914.1557930
[17]
Mehta, S., Banati, H. 2014. Context aware filtering using social behavior of frogs. Swarm and Evolutionary Computation. 17: 25--36, https://doi.org/10.1016/j.swevo.2014.02.003
[18]
Nikolaos, K., Marios P. 2013. Using online consumer reviews as a source for demographic recommendations: A case study using online travel reviews. Expert Syst. Appl. 40(14): 5507--5515, https://doi.org/10.1016/j.eswa.2013.03.046
[19]
Ortega, F., Sánchez, J.-L., Bobadilla, J., Gutiérrez, A. 2013. Improving collaborative filtering-based recommender systems results using Pareto dominance, Inf. Sci. 239. 50--61, https://doi.org/10.1016/j.ins.2013.03.011
[20]
Pazzani, M., Billsus, D. 2007. Content-based recommendation systems, in: The Adaptive Web, LNCS, vol. 25, Springer, Berlin/Heidelber, pp. 325--341, https://doi.org/10.1007/978-3-540-72079-9 10
[21]
Resnick, P., Varian, H.R. 1997. Recommender systems. Communications of the ACM. 40(3): 56--58, https://doi.org/10.1145/245108.245121
[22]
Shani, G., Heckerman, D., Brafman, R. I. 2005. An MDP-based recommender system. Journal of Machine Learning Research. 6: 1265--1295,
[23]
Shikha, M., Hema, B. 2014. Context aware filtering using social behavior of frogs. Swarm and Evolutionary Computation 17: 25--36. https://doi.org/10.1016/j.swevo.2014.02.003
[24]
Wang, C. H. 2015. Using quality function deployment to conduct vendor assessment and supplier recommendation for business-intelligence systems. Computers & Industrial Engineering, 84: 24--31, https://doi.org/10.1016/j.cie.2014.10.005
[25]
Wood, A. D. 2016. Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. Journal of Natural Gas Science and Engineering. 28: 594--612, https://doi.org/10.1016/j.jngse.2015.12.021
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- Weighted utility based recommender for e-procurement in handicraft communities
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December 2019
709 pages
ISBN:9781450371797
DOI:10.1145/3366030
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- JKU: Johannes Kepler Universität Linz
- @WAS: International Organization of Information Integration and Web-based Applications and Services
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Association for Computing Machinery
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Published: 22 February 2020
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iiWAS2019
iiWAS2019: The 21st International Conference on Information Integration and Web-based Applications & Services
December 2 - 4, 2019
Munich, Germany
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