Shopping and Basket Analysis by Using an Improved Apriori Algorithm in WEKA
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Abstract
In the past years, e-commerce and online shopping grew fast. It became more helpful by letting people buy the desired product online. Also, to help their users to find the product of their desire easily and make the process simpler, the online shopping websites use some kinds of an algorithm to provide recommendation systems. Often, these systems use techniques like basket analyzing and association rules which is finding the relation between the products together or between users too, so apriori algorithm is one of the famous ones among the recommendation systems. Although it has some limitations while implementing which makes the algorithm less confident or even useless, Let us assume we have 100K records in the sold item list in a system in which about 10K refers to the customers buying only one or two items in their purchase. Therefore, this ten per cent will not affect finding the relation between the items, at the same time these records will make the system less efficient and take more time to analyze, in this paper, we try to show how we can improve the apriori algorithm efficiency and accuracy by some preprocessing on the dataset before applying apriori algorithm by eliminating the unnecessary records, this process helps to make the algorithm better because of reducing the number of transactions, hence finding strong relationships between items easier for the rest of the records.
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References
Fayyad, U. M., Piatestky-Shapiro, G., Smyth, P. “From Data Mining to Knowledge Discovery: An Overview”, AAAI Press / The MIT Press, pp. 1-34, 1996
M. P. Robillard and R. J. Walker., 2014. An Introduction to Recommendation Systems in Software Engineer-ing. In Recommendation Systems in Software Engineering (pp. 01 -11). Springer, Berlin, Heidelberg.
Dhawan, S. and Singh, K., 2015. High rating recent preferences based recommendation system. Procedia Computer Science, 70, pp.259-264.
Resnick, P. and Varian, H.R., 1997. Recommender systems. Communications of the ACM, 40(3), pp.56-58.
Resnick, P. and Varian, H.R., 1997. Recommender systems. Communications of the ACM, 40(3), pp.56-58.
Dhawan, S. and Singh, K., 2015. High rating recent preferences based recommendation system. Procedia Computer Science, 70, pp.259-264.
Paranjape-Voditel, P. and Deshpande, U., 2013. A stock market portfolio recommender system based on association rule mining. Applied Soft Computing, 13(2), pp.1055-1063.
Zhang, Z. and Qian, S., 2012. The research of e-commerce recommendation system based on collaborative filtering technology. In Advances in Computer Science and Information Engineering (pp. 507-512). Springer, Berlin, Heidelberg.
Wei, C.P., Shaw, M.J. and Easley, R.F., 2016. A survey of recommendation systems in electronic commerce. In E-Service: new directions in theory and practice (pp. 180-211). Routledge.
Schafer, J.B., Konstan, J. and Riedl, J., 1999, November. Recommender systems in e-commerce. In Proceedings of the 1st ACM conference on Electronic commerce (pp. 158-166).
Sarwar, B., Karypis, G., Konstan, J. and Riedl, J., 2000, October. Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM Conference on Electronic Commerce (pp. 158-167).
Venkatesan, R. and Sabari, A., 2020. ISSUES IN VARIOUS RECOMMENDER SYSTEM IN E-COMMERCE–A SURVEY. Journal of Critical Reviews, 7(7), pp.604-608.
Linden, G., Smith, B. and York, J., 2003. Amazon. com recommendations: Item-to-item collaborative filter-ing. IEEE Internet computing, 7(1), pp.76-80.
McCarthy, J.F. and Anagnost, T.D., 1998, November. MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In Proceedings of the 1998 ACM conference on Computer supported co-operative work (pp. 363-372).
Chao, D.L., Balthrop, J. and Forrest, S., 2005, November. Adaptive radio: achieving consensus using negative preferences. In Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work (pp. 120-123).
Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Li, X., Cosley, D., Frankowski, D., Terveen, L., Ra-shid, A.M. and Resnick, P., 2005. Using social psychology to motivate contributions to online communi-ties. Journal of Computer‐Mediated Communication, 10(4), pp.00-00.
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J., 1994, October. Grouplens: An open ar-chitecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 175-186).
Guo, Y., Wang, M. and Li, X., 2017. Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Industrial Management & Data Systems.
Agrawal, R. and Srikant, R., 1994, September. Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).