Recipe recommendation using ingredient networks

Proceedings of the 4th Annual ACM Web Science Conference, WebSci'12

Volumn volume, Issue , 2012, Pages 298-307

  Teng, Chun Yuen ^a   Lin, Yu Ru ^b,c   Adamic, Lada A ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b HARVARD UNIVERSITY   (United States)

^c NORTHEASTERN UNIVERSITY   (United States)

Author keywords

Ingredient networks; Recipe recommendation

Indexed keywords

COOKING METHODS; COOKING RECIPE; HUMAN ACTIVITIES; NETWORK CAPTURE; RECIPE RECOMMENDATION;

COOKING;

EID: 84869069156     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2380718.2380757     Document Type: Conference Paper

References (19)

1. Ahn, Y., Ahnert, S., Bagrow, J., and Barabasi, A. Flavor network and the principles of food pairing. Bulletin of the American Physical Society 56 (2011).
2. Cortes, C., and Vapnik, V. Support-vector networks. Machine learning 20, 3 (1995), 273-297.
3. Forbes, P., and Zhu, M. Content-boosted matrix factorization for recommender systems: Experiments with recipe recommendation. Proceedings of Recommender Systems (2011).
4. Freyne, J., and Berkovsky, S. Intelligent food planning: personalized recipe recommendation. In IUI, ACM (2010), 321-324.
5. Friedman, J. Stochastic gradient boosting. Computational Statistics & Data Analysis 38, 4 (2002), 367-378.
6. Friedman, J., Hastie, T., and Tibshirani, R. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28 (1998), 2000.
7. Geleijnse, G., Nachtigall, P., van Kaam, P., and Wijgergangs, L. A personalized recipe advice system to promote healthful choices. In IUI, ACM (2011), 437-438.
8. Hastie, T., Tibshirani, R., Friedman, J., and Franklin, J. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer 27, 2 (2005).
9. Kamieth, F., Braun, A., and Schlehuber, C. Adaptive implicit interaction for healthy nutrition and food intake supervision. Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments (2011), 205-212.
10. Kinouchi, O., Diez-Garcia, R., Holanda, A., Zambianchi, P., and Roque, A. The non-equilibrium nature of culinary evolution. New Journal of Physics 10 (2008), 073020.
11. Lu, Y., Peng, F., Li, X., and Ahmed, N. Coupling feature selection and machine learning methods for navigational query identification. In CIKM, ACM (2006), 682-689.
12. Rombauer, I., Becker, M., Becker, E., and Maestro, L. Joy of cooking. Scribner Book Company, 1997.
13. Rosvall, M., and Bergstrom, C. Maps of random walks on complex networks reveal community structure. PNAS 105, 4 (2008), 1118.
14. Shidochi, Y., Takahashi, T., Ide, I., and Murase, H. Finding replaceable materials in cooking recipe texts considering characteristic cooking actions. In Proc. of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities, ACM (2009), 9-14.
15. Svensson, M., Höök, K., and Cöster, R. Designing and evaluating kalas: A social navigation system for food recipes. ACM Transactions on Computer-Human Interaction (TOCHI) 12, 3 (2005), 374-400.
16. Ueda, M., Takahata, M., and Nakajima, S. User's food preference extraction for personalized cooking recipe recommendation. Proc. of the Second Workshop on Semantic Personalized Information Management: Retrieval and Recommendation (2011).
17. Wang, L., Li, Q., Li, N., Dong, G., and Yang, Y. Substructure similarity measurement in chinese recipes. In WWW, ACM (2008), 979-988.
18. Wikipedia. Outline of food preparation, 2011. [Online; accessed 22-Oct-2011].
19. Zhang, Q., Hu, R., Mac Namee, B., and Delany, S. Back to the future: Knowledge light case base cookery. In Proc. of The 9th European Conference on Case-Based Reasoning Workshop (2008), 15.