A Hybrid Recommendation System Based On Association Rules
Authors: Ahmed Mohammed K. Alsalama
Recommendation systems are widely used in e-commerce applications. The engine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose1 a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of Users and a small number of Items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1337773Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3511
 B., Shneiderman (2008). Copernican challenges face those who suggest that collaboration, not computation are the driving energy for socio-technical systems that characterize Web 2.0. Science, 319, 1349-1350.
 E.,Vozalis, and K. G.,Margaritis (2003, September). Analysis of recommender systems algorithms. In Proceedings of the 6th Hellenic European Conference on Computer Mathematics and its Applications (HERCMA-2003), Athens, Greece.
 G.,Adomavicius, and A.,Tuzhilin (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734-749.
 Z.,Huang, D.,Zeng, and H., Chen (2004). A unified recommendation framework based on Probabilistic Relational Models. In Fourteenth Annual Workshop on Information Technologies and Systems (WITS) (pp. 8-13).
 J.,Han, and M.,Kamber (2006). Data mining: concepts and techniques (2nd ed.). Amsterdam: Elsevier .
 M.,Hegland (2007). The apriori algorithma tutorial. Mathematics and Computation in Imaging Science and Information Processing, 11, 209-262.
 G.,Linden, B.,Smith, and J.,York (2003). Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.
 A.,da Silva Meyer, A. F.,Garcia, A. P.,de Souza, and C. L.,de Souza (2004). Comparison of similarity coefficients used for cluster analysis with dominant markers in maize (Zea mays L.). Genetics and Molecular Biology, 27, 83-91.
 X.,Su, and T. M.,Khoshgoftaar (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 4.
 B.,Amini, R.,Ibrahim, and M.S.,Othman (2011). Discovering the impact of knowledge in recommender systems: A comparative study. arXiv preprint arXiv:1109.0166.
 M. A.,Ghazanfar, and A.,Prugel-Bennett (2010, January). A scalable, accurate hybrid recommender system. In Knowledge Discovery and Data Mining, 2010. WKDD’10. Third International Conference on (pp. 94-98). IEEE.
 T.,Tran, and R.,Cohen (2000, July). Hybrid recommender systems for electronic commerce. In Proc. Knowledge-Based Electronic Markets, Papers from the AAAI Workshop, Technical Report WS-00-04, AAAI Press.
 R.,Perego, S.,Orlando, and P.,Palmerini (2001). Enhancing the apriori algorithm for frequent set counting. Data Warehousing and Knowledge Discovery, 71-82.
 B.,Sigurbjrnsson, and R.,Van Zwol (2008, April). Flickr tag recommendation based on collective knowledge. In Proceedings of the 17th international conference on World Wide Web (pp. 327-336). ACM.
 P.,Tan, M.,Steinbach, and V.,Kumar (2005). Introduction to data mining. Boston: Pearson Addison Wesley.
 MovieLens Data Sets. (2011, August 8). GroupLens Research. Retrieved November 18, 2012, from http://www.grouplens.org/node/73
 Weka 3 - Data Mining with Open Source Machine Learning Software in Java . (n.d.). Machine Learning Group at University of Waikato . Retrieved November 18, 2012, from http://www.cs.waikato.ac.nz/ml/weka
 About the Eclipse Foundation. (n.d.). Eclipse. Retrieved November 18, 2012, from http://www.eclipse.org/
 B.,Sarwar, G.,Karypis, J.,Konstan,and J.,Riedl (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285-295). ACM.
 J. L.,Herlocker, J. A.,Konstan, L. G.,Terveen, and J. T.,Riedl (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), 5-53.
 G.,Shani, and A.,Gunawardana (2011). Evaluating recommendation systems. Recommender Systems Handbook, 257-297.
 Y.,Koren (2008, August). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 426-434). ACM.
 Alsalama, Ahmed (2013). A Hybrid Recommendation System Based on Association Rules. Masters Theses and Specialist Projects. Paper 1250. http://digitalcommons.wku.edu/theses/1250