{"title":"A Hybrid Recommendation System Based On Association Rules","authors":"Ahmed Mohammed K. Alsalama","volume":97,"journal":"International Journal of Computer and Information Engineering","pagesStart":55,"pagesEnd":63,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10000147","abstract":"
Recommendation systems are widely used in
\r\ne-commerce applications. The engine of a current recommendation
\r\nsystem recommends items to a particular user based on user
\r\npreferences and previous high ratings. Various recommendation
\r\nschemes such as collaborative filtering and content-based approaches
\r\nare used to build a recommendation system. Most of current
\r\nrecommendation systems were developed to fit a certain domain such
\r\nas books, articles, and movies. We propose1 a hybrid framework
\r\nrecommendation system to be applied on two dimensional spaces
\r\n(User × Item) with a large number of Users and a small number
\r\nof Items. Moreover, our proposed framework makes use of both
\r\nfavorite and non-favorite items of a particular user. The proposed
\r\nframework is built upon the integration of association rules mining
\r\nand the content-based approach. The results of experiments show
\r\nthat our proposed framework can provide accurate recommendations
\r\nto users.<\/p>\r\n","references":"[1] B., Shneiderman (2008). Copernican challenges face those who\r\nsuggest that collaboration, not computation are the driving energy\r\nfor socio-technical systems that characterize Web 2.0. Science, 319,\r\n1349-1350.\r\n[2] E.,Vozalis, and K. G.,Margaritis (2003, September). Analysis of\r\nrecommender systems algorithms. In Proceedings of the 6th Hellenic\r\nEuropean Conference on Computer Mathematics and its Applications\r\n(HERCMA-2003), Athens, Greece.\r\n[3] G.,Adomavicius, and A.,Tuzhilin (2005). Toward the next generation\r\nof recommender systems: A survey of the state-of-the-art and possible\r\nextensions. Knowledge and Data Engineering, IEEE Transactions on,\r\n17(6), 734-749.\r\n[4] Z.,Huang, D.,Zeng, and H., Chen (2004). A unified recommendation\r\nframework based on Probabilistic Relational Models. In Fourteenth Annual\r\nWorkshop on Information Technologies and Systems (WITS) (pp. 8-13).\r\n[5] J.,Han, and M.,Kamber (2006). Data mining: concepts and techniques (2nd\r\ned.). Amsterdam: Elsevier .\r\n[6] M.,Hegland (2007). The apriori algorithma tutorial. Mathematics and\r\nComputation in Imaging Science and Information Processing, 11, 209-262.\r\n[7] G.,Linden, B.,Smith, and J.,York (2003). Amazon. com recommendations:\r\nItem-to-item collaborative filtering. Internet Computing, IEEE, 7(1),\r\n76-80.\r\n[8] A.,da Silva Meyer, A. F.,Garcia, A. P.,de Souza, and C. L.,de Souza\r\n(2004). Comparison of similarity coefficients used for cluster analysis\r\nwith dominant markers in maize (Zea mays L.). Genetics and Molecular\r\nBiology, 27, 83-91.\r\n[9] X.,Su, and T. M.,Khoshgoftaar (2009). A survey of collaborative filtering\r\ntechniques. Advances in Artificial Intelligence, 2009, 4.\r\n[10] B.,Amini, R.,Ibrahim, and M.S.,Othman (2011). Discovering the impact\r\nof knowledge in recommender systems: A comparative study. arXiv\r\npreprint arXiv:1109.0166.\r\n[11] M. A.,Ghazanfar, and A.,Prugel-Bennett (2010, January). A scalable,\r\naccurate hybrid recommender system. In Knowledge Discovery and Data\r\nMining, 2010. WKDD\u201910. Third International Conference on (pp. 94-98).\r\nIEEE.\r\n[12] T.,Tran, and R.,Cohen (2000, July). Hybrid recommender systems for\r\nelectronic commerce. In Proc. Knowledge-Based Electronic Markets,\r\nPapers from the AAAI Workshop, Technical Report WS-00-04, AAAI\r\nPress.\r\n[13] R.,Perego, S.,Orlando, and P.,Palmerini (2001). Enhancing the apriori\r\nalgorithm for frequent set counting. Data Warehousing and Knowledge\r\nDiscovery, 71-82.\r\n[14] B.,Sigurbjrnsson, and R.,Van Zwol (2008, April). Flickr tag\r\nrecommendation based on collective knowledge. In Proceedings of\r\nthe 17th international conference on World Wide Web (pp. 327-336).\r\nACM.\r\n[15] P.,Tan, M.,Steinbach, and V.,Kumar (2005). Introduction to data mining.\r\nBoston: Pearson Addison Wesley.\r\n[16] MovieLens Data Sets. (2011, August 8). GroupLens Research. Retrieved\r\nNovember 18, 2012, from http:\/\/www.grouplens.org\/node\/73\r\n[17] Weka 3 - Data Mining with Open Source Machine Learning Software in\r\nJava . (n.d.). Machine Learning Group at University of Waikato . Retrieved\r\nNovember 18, 2012, from http:\/\/www.cs.waikato.ac.nz\/ml\/weka\r\n[18] About the Eclipse Foundation. (n.d.). Eclipse. Retrieved November 18,\r\n2012, from http:\/\/www.eclipse.org\/\r\n[19] B.,Sarwar, G.,Karypis, J.,Konstan,and J.,Riedl (2001, April). Item-based\r\ncollaborative filtering recommendation algorithms. In Proceedings of the\r\n10th international conference on World Wide Web (pp. 285-295). ACM.\r\n[20] J. L.,Herlocker, J. A.,Konstan, L. G.,Terveen, and J. T.,Riedl\r\n(2004). Evaluating collaborative filtering recommender systems. ACM\r\nTransactions on Information Systems (TOIS), 22(1), 5-53.\r\n[21] G.,Shani, and A.,Gunawardana (2011). Evaluating recommendation\r\nsystems. Recommender Systems Handbook, 257-297.\r\n[22] Y.,Koren (2008, August). Factorization meets the neighborhood: a\r\nmultifaceted collaborative filtering model. In Proceeding of the 14th\r\nACM SIGKDD international conference on Knowledge discovery and data\r\nmining (pp. 426-434). ACM.\r\n[23] Alsalama, Ahmed (2013). A Hybrid Recommendation System Based on\r\nAssociation Rules. Masters Theses and Specialist Projects. Paper 1250.\r\nhttp:\/\/digitalcommons.wku.edu\/theses\/1250","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 97, 2015"}