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Building a Recommendation System for Online Shopping Based on Item-Based Collaborative Filtering

dc.contributor.authorBadriyah, Tessy
dc.contributor.authorPrasetyaningrum, Ira
dc.contributor.authorAdhi P., Basik
dc.contributor.authorSyarif, Iwan
dc.date.accessioned2015-12-05T08:06:44Z
dc.date.available2015-12-05T08:06:44Z
dc.date.issued2015-12-07
dc.identifier.citation[1] Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Paper presented at the Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Madison, Wisconsin. [2] Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Commun. ACM, 35(12), 61-70. doi:10.1145/138859.138867 [3] Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: applying collaborative filtering to Usenet news. Commun. ACM, 40(3), 77-87. doi:10.1145/245108.245126 [4] Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. Paper presented at the Proceedings of the 1994 ACM conference on Computer supported cooperative work, Chapel Hill, North Carolina, USA. [5] Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. Paper presented at the Proceedings of the 10th international conference on World Wide Web, Hong Kong, Hong Kong. [6] Schafer, J. B., Konstan, J., & Riedl, J. (1999). Recommender systems in e-commerce. Paper presented at the Proceedings of the 1st ACM conference on Electronic commerce, Denver, Colorado, USA [7] Lemire, Daniel and Maclachlan, Anna. Slope One Predictors for Online Rating-Based Collaborative Filtering. California : SIAM Data Mining (SDM'05), 2005. [8] GroupLens. datasets. grouplens. [Online] 2014. http://grouplens.org/datasets/movielens/. [9] Zacharski, Ron. A Programmer's Guide to Data Mining : The Ancient Art of the Numerati. guidetodatamining.com. [Online] 2012. http://guidetodatamining.comin_ID
dc.identifier.issn2477-3328
dc.identifier.urihttp://hdl.handle.net/11617/6318
dc.description.abstractThis research applied an innovation in developing online shopping, using recommendation system. Recommendation System applies finding knowledge technique which is called item-based Collaborative Filtering. This works with by building information about items that are preferred by the customers. Collaborative Filtering filters data based on similarities or certain characteristics, so that the system is able to provide information based on patterns from a certain group of data that are almost the same. With recommendation system, customers could benefit from the recommended items which they may favour, generated automatically by the system. It is hoped that it could improve the convenience to shop and reduce the time needed by customers to search for items. Therefore it could increase the competitiveness of online shops that use a recommendation system.in_ID
dc.language.isoenin_ID
dc.publisherUniversitas Muhammadiyah Surakartain_ID
dc.subjectonline shoppingin_ID
dc.subjectrecommendation systemin_ID
dc.subjectitem-based collaborative filteringin_ID
dc.titleBuilding a Recommendation System for Online Shopping Based on Item-Based Collaborative Filteringin_ID
dc.typeArticlein_ID


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