Abstract
Most of existing applications for locating and retrieving information are currently oriented towards offering personalized recommendations using well-known recommender techniques as content-based or collaborative filtering. Nevertheless, automatic information retrieval approaches still lack of an efficient analysis, integration and adaptation of the retrieved information. This can be observed mainly when information comes from different sources. In this way, the application of intelligent techniques can offer an interesting approach for solving this kind of complex processes. This paper employs an evolutive approach in order to improve the retrieval process of correct nutritional information of ingredients in an on-line recommender system of cooking recipes. The proposed algorithm has been tested over real data. Moreover, some heuristics have been included in order to improve the obtained results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Available in http://receteame.com/.
- 2.
Database available in http://ndb.nal.usda.gov/ndb/foods.
- 3.
NoSQL database which uses a JSON document structure type, available in https://www.mongodb.org/.
References
Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms and Their Application, pp. 14–21. L. Erlbaum Associates Inc., Hillsdale (1987)
Brindle, A.: Genetic algorithms for function optimization. Ph.D. thesis, University of Alberta, Department of Computer Science, Edmonton (1981)
Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)
Chakraborty, M., Chakraborty, U.K.: An analysis of linear ranking and binary tournament selection in genetic algorithms. In: Proceedings of the International Conference on Information, Communications and Signal Processing ICICS 1997, pp. 407–411. IEEE, Singapore, 9–12 September 1997
Eshelman, L.J., David Schaffer, J.: Real-coded genetic algorithms and interval-schemata. In: Darrel Whitley, L. (ed.) Foundations of Genetic Algorithms 2, pp. 187–202. Morgan Kaufmann Publishers, San Mateo (1993)
Goldberg, D.E., Deb, K.: A comparative analisys of selection schemes used in genetic algorithms, pp. 69–93. Morgan Kaufmann, San Mateo (1991)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)
Herrera, F., Lozano, E., Pé, E., Sánchez, A.M., Villar, P.: Multiple crossover per couple with selection of the two best offspring: an experimental study with the blx-\(\alpha \) crossover operator for real-coded genetic algorithms. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527. Springer, Heidelberg (2002)
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms. operators and tools for behavioural analysis. Artif. Intell. Rev. 12, 265–319 (1998)
Matei, O., Contras, D.: Advanced genetic operators in the context of evolutionary ontology. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 9–14, May 2015
Matei, O., Contras, D., Pop, P.: Applying evolutionary computation for evolving ontologies. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 1520–1527, July 2014
Michalewicz, Z.: Genetic Algorithms+Data Structures=Evolution Programs. Springer, New York (1992)
Ortiz, D., Hervas, C., Muñoz, J.: Genetic algorithm with crossover based on confidence interval as an alternative to traditional nonlinear regression methods. In: 9th European Symposium On Artificial Neural Networks. ESANN 2001, pp. 193–198, Bruges, Belgium (2001)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Peñaranda, C., Valero, S., Julian, V., Palanca, J. (2016). Application of Genetic Algorithms and Heuristic Techniques for the Identification and Classification of the Information Used by a Recipe Recommender. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-32034-2_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32033-5
Online ISBN: 978-3-319-32034-2
eBook Packages: Computer ScienceComputer Science (R0)