Semantic Reasoning-Based Chinese Recipe Recommender System

Article Preview

Abstract:

For the blank of the recommender system for chinese recipes, this paper uses OWLS-WSDL to build a semantic reasoning-based chinese recipe recommender system. This system through the tool of Protégé to establish the ontology of chinese recipes and then add rules for ontology reasoning. On this basis bring out a catering algorithm, using Euclidean distance and Jaccard to calculate the similarity between the dishes. According to the similarity as well as user preference, provides a quick means of siding dishes for users.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 718-720)

Pages:

1998-2004

Citation:

Online since:

July 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] RESNICK P, IAKOVOU N, SUSHAK M, et al. GroupLens: An Open Architecture for Collaborative Filtering of Netnews [C] // Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, Chapel Hill, NC: Pages 175-186

DOI: 10.1145/192844.192905

Google Scholar

[2] HILL W, STEAD L, ROSENSTEIN M, et al. Recommending and evaluating choices in a virtual community of Use [C] // Proceedings of SIGCHI conference on Human Factors in Computing Systems. New York: ACM Press, 1995: 194-201

DOI: 10.1145/223904.223929

Google Scholar

[3] RESNICK P, Varian HR. Recommender systems. Communications of the ACM, 1997, 40(3): 56-58

Google Scholar

[4] MOONEY R J, BENNETT P N, ROY L. Book recommending using test categorization with extracted information [C] //Proceedings of AAAI-98/ICML-98 Workshop on Learning for Text Categorization and
the AAAI-98 Workshop on Recommender Systems, pp.49-54 and pp.70-74

Google Scholar

[5] PAZZANI M, BILLSUS D. Learning and Revising User Profiles: The Identification of Interesting Web Sites [J]. Machine Learning, 1977, 27(3): 313-331.

DOI: 10.1023/a:1007369909943

Google Scholar

[6] MOSTAFA J, LAM W. Automatic classification using supervised learning in a medical document filtering application [J]. Information Processing and Management,2000, 36(3): 415-444

DOI: 10.1016/s0306-4573(99)00033-3

Google Scholar

[7] SUNGSHUN W, BINSHAN L, WENTIEN C. Using contextual information and multidimensional approach for recommendation [J]. Expert System with Applications, 2009, 36(2): 1268-1279

Google Scholar

[8] PANACIOTIS S, ALEXANDROS N, APSTOLOS N, et al. Collaborative recommender system: combing effectiveness and efficiency [J]. Expert System with Applications, 2007, 34(4): 2995-3013

Google Scholar

[9] LEUNG C W, CHAN S C, CHUNG F, et al. An empirical study of a cross-level association rule mining approach to cold-start recommendations [J].Knowledge-based Systems, 2008, 21(7): 515-529

DOI: 10.1016/j.knosys.2008.03.012

Google Scholar

[10] SOMLO G, HOWE A. Adaptive lightweight text filtering [C] // Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis. 2001: 319-329.

DOI: 10.1007/3-540-44816-0_32

Google Scholar

[11] ROBERTSON S. Threshold setting and performance optimization in adaptive filtering [J]. Information Retrieval, 2002, 5(2-3): 239-256.

Google Scholar

[12] ZHANG Yi, CALLAN J. Maximum likelihood estimation for filtering thresholds [C] // Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2001: 294-302.

DOI: 10.1145/383952.384012

Google Scholar

[13] Bao Wen, Li Guan Jun. Ontology storage management technology research. Chinese science and technology papers online[EB/OL][2007-06-26].http://www.paper.edu.cn/downloadpaper.php?serial_number=200704-631

Google Scholar

[14] Rudi L, Paul M B. The Google Similarity Distance [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(3): 370-383.

Google Scholar

[15] SPERTUS E, SAHAMI M, BUYUKKOKTEN O. Evaluating similarity measures: a large-scale study in the orkut social network [C] // Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2005), (2005)

DOI: 10.1145/1081870.1081956

Google Scholar