An Adapted Approach for User Profiling in a Recommendation System: Application to Industrial Diagnosis
DOI:
https://doi.org/10.9781/ijimai.2018.06.003Keywords:
Recommendation Systems, DSS, Twitter, Collaborative Filtering, Industrial DiagnosisAbstract
In this paper, we propose a global architecture of a recommender tool, which represents a part of an existing collaborative platform. This tool provides diagnostic documents for industrial operators. The recommendation process considered here is composed of three steps: Collecting and filtering information; Prediction or recommendation step; evaluating and improvement. In this work, we focus on collecting and filtering step. We mainly use information result from collaborative sessions and documents describing solutions that are attributed to the complex diagnostic problems. The developed tool is based on collaborative filtering that operates on users' preferences and similar responses.Downloads
References
G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed. vol. 3, J. Peters, Ed. New York: McGraw-Hill, 1964, pp. 15–64.
F.Z. Benkaddour, N. Taghezout, B. Ascar, “Towards a Novel Approach for Enterprise Knowledge Capitalization Utilizing an Ontology and Collaborative Decision-Making: Application to Inotis Enterprise”. International Journal of Decision Support System Technology (IJDSST), 2016, vol. 8, no 1, p. 1-24.
X. Su, and M. Khoshgoftaar. “A survey of collaborative filtering techniques”. Advances in Artificial Intelligence, 2009, vol. 2009, p. 4.
H. Lu, C. Chen, M. Kong, H. Zhang, and Z. Zhao, “Social recommendation via multi-view user preference learning”. Neurocomputing, 2016, vol. 216, p. 61-71.
J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items”. Expert Systems with Applications, 2017, vol. 69, p. 29-39.
A. Martin, P. Zarate, and G. Camillieri, “A Multi-Criteria Recommender System Based on Users’ Profile Management”. In: Multiple Criteria Decision Making. Springer International Publishing, 2017. p. 83-98.
O., Sanjuan Martínez, C., Pelayo G-Bustelo, R., González Crespo, and E., Torres Franco, “Using recommendation system for e-learning environments at degree level”. International Journal of Artificial Intelligence and Interactive Multimedia, 2009, vol. 1, no 2. p. 67–70.
V. Satish Kumar, and P. Nymphia, “Survey on content based recommendation system”. Int. J. Comput. Sci. Inf. Technol, 2016, vol. 7, no 1, p. 281-284.
E. R, Nuñez-Valdez, J.M.C., Lovelle, O., Sanjuan-Martinez, C.E., Montenegro-Marin, and G. Infante-Hernandez. “Social voting techniques: a comparison of the methods used for explicit feedback in recommendation systems”. International Journal of Interactive Multimedia and Artificial Intelligence, 2011, vol. 1, no 4.
A.M., Dakhel, H.T., Malazi, and M., Mahdavi, “A social recommender system using item asymmetric correlation”. Applied Intelligence, 2017, p. 1-14.
J. Mathieu, Dyson, Mark, Callaway, and Duncan, “Using residential electric loads for fast demand response: The potential resource and revenues, the costs, and policy recommendations”. In: ACEEE Summer Study on Energy Efficiency in Buildings. 2012. p. 189-203.
E. R, Núñez-Valdéz, J.M.C., Lovelle, O.S., Martínez, V., García-Díaz, P.O., De Pablos, and C.E.M., Marín, “Implicit feedback techniques on recommender systems applied to electronic books”. Computers in Human Behavior, 2012, vol. 28, no 4. p. 1186-1193.
E. R, Nuñez-Valdez, J.M.C., Lovelle, J.M., Cueva, G.I., Hernández, A.J., A.J., Fuente, and J.E., Labra-Gayo, “Creating recommendations on electronic books: A collaborative learning implicit approach”. Computers in Human Behavior, 2015, vol. 51, p. 1320-1330.
R., Hastings, and M., Randolph, from https://www.netflix.com/
, created in 1997.
S., Chen, C., Hurley, and J. Karim, from https://www.youtube.com/
, created in 2005.
M., Zuckerberg, and S. Sandberg, from https://www.facebook.com/
, created in 2004.
Mugly, and C. Petersen, from http://www.tripadvisor.fr
, created in 2000.
Book Crossing, from: http://www.BookCrossing.com
, visited in 01/02/2017 (2001).
Google, “Google scholar”, from: https://scholar.google.com
, created in 2004.
F., Abel, Q., Gao, G.J., Houben, and K., Tao. Analyzing user modeling on twitter for personalized news recommendations. In International Conference on User Modeling, Adaptation, and Personalization (pp. 1-12). 2011, July. Springer, Berlin, Heidelberg.
Bezos, J., “Amazon.com”, from: https://www.amazon.com
, created in 1994. Visited in 10/01/2017.
Z. Wen, Recommendation Systems based on Collaborative filtering. CS229 Lecture Notes, 2008.
W. Liang, G. Lu, X. Ji. J. Li, and D. Yuan, “Difference Factor’ KNN Collaborative Filtering Recommendation Algorithm”. In: International Conference on Advanced Data Mining and Applications. Springer, Cham, 2014. p. 175-184.
U. Sharda and P. Maes, “Social information filtering: algorithms for automating “word of mouth””. In: Proceedings of the SIGCHI conference on Human factors in computing systems. ACM Press/Addison-Wesley Publishing Co., 1995. p. 210-217.
S. Holmes, “RMS Error”, from http://statWeb.stanford.edu/~susan/courses/s60/split/node60.html
. Visited in 02/04/2017 (2000).
F.Z. Benkaddour, N. Taghezout, B. Ascar, “Novel Agent Based-approach for Industrial Diagnosis: A Combined Use between Case-based Reasoning and Similarity Measures”. International Journal of Interactive Multimedia & Artificial Intelligence, 2016, vol. 4, no 2.
I. Esslimani, A. Brun, and A. Boyer, from social networks to behavioral networks in recommender systems. In: Social Network Analysis and Mining, 2009. ASONAM’09. International Conference on Advances in. IEEE, 2009. p. 143-148.
I. Esslimani, Vers une approche comportementale de recommandation: apport de l’analyse des usages dans un processus de personnalisation. 2010. Thèse de doctorat. Université Nancy II.
Schrepp, M., A. Hinderks, and J. Thomaschewski. Design and Evaluation of a Short Version of the User Experience Questionnaire (UEQ-S), International Journal of Interactive Multimedia and Artificial Intelligence, 2017, vol. 4 no. 6, pp. 103-108.
Schrepp, M., A. Hinderks, and J. Thomaschewski. Construction of a Benchmark for the User Experience Questionnaire (UEQ), International Journal of Interactive Multimedia and Artificial Intelligence, 2017, vol. 4 no. 4, pp. 40-44.
Bader, F., E. M. Schön, and J. Thomaschewski. Heuristics Considering UX and Quality Criteria for Heuristics, International Journal of Interactive Multimedia and Artificial Intelligence, 2017, vol. 4 no. 6, pp. 48-53.
Q., Yuan, C., Li, S., Zhao, “Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation”. In: Proceedings of the 5th ACM conference on recommender systems. ACM, 2011, pp 245–252.
J. Bobadilla, F., Ortega, A., Hernando, A., Gutierrez, “Recommender systems survey.” Knowl-Based Syst vol. 46, 2013, pp.109–132.
Nagard, E., L., “Use the Twitter API to collect tweets with Talend” from: http://www.erwanlenagard.com/general/tutoriel-utiliser-lapi-twitter-pourcollecter-des-tweets-sans-coder-avec-talend-1029
. Accessed: 2017-05-20
Sentiment analysis. http://text-processing.com/docs/sentiment.html. Accessed: 2017-05-29
Downloads
Published
-
Abstract26
-
PDF31
Submit Article
Information
