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American Journal of Computer Science and Information Engineering

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Introduction to a Framework of E-commercial Recommendation Algorithms

American Journal of Computer Science and Information Engineering
Vol.2 , No. 4, Publication Date: Sep. 28, 2015, Page: 33-44

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Authors

[1]	Loc Nguyen, Board of Directors, Sunflower Soft Company, Ho Chi Minh City, Vietnam.

Abstract

Recommendation algorithm is very important for e-commercial websites when it can recommend online customers favorite products, which results out an increase in sale revenue. I propose the framework of e-commercial recommendation algorithms. This is a middleware framework or “operating system” for e-commercial recommendation software, which support scientists and software developers build up their own recommendation algorithms based on this framework with low cost, high achievement and fast speed.

Keywords

Recommendation Algorithm, Recommendation Server, Middleware Framework

Reference

[01]	Brožovský, L. (2006, August). ColFi - Recommender System for a Dating Service. (V. Petříček, Ed.) Prague, Czech Republic: Charles University in Prague.
[02]	Caraciolo, M., Melo, B., & Caspirro, R. (2011). Crab - Recommender systems in Python. Muriçoca Labs.
[03]	Chen, T., Zhang, W., Lu, Q., Chen, K., Zheng, Z., Yu, Y., et al. (2012). SVDFeature: A Toolkit for Feature-based Collaborative Filtering. 1.2.2. China: APEX Data & Knowledge Management Lab.
[04]	Dato-Team. (2013, October 15). GraphLab Create™. (C. Guestrin, Ed.) Seattle, Washington, USA: Dato, Inc.
[05]	Ekstrand, M., Kluver, D., He, L., Kolb, J., Ludwig, M., & He, Y. (2013). LensKit - Open-Source Tools for Recommender Systems. Group Lens Research, University of Minnesota.
[06]	Gantner, Z., Rendle, S., Drumond, L., & Freudenthaler, C. (2013). MyMediaLite Recommender System Library. 3.10. University of Hildesheim and The European Commission 7th Framework Programme.
[07]	GroupLens. (1998, April 22). MovieLens datasets. (GroupLens Research Project, University of Minnesota, USA) Retrieved August 3, 2012, from GroupLens Research website: http://grouplens.org/datasets/movielens.
[08]	Hahsler, M. (2014, 08 18). recommenderlab: Lab for Developing and Testing Recommender Algorithms. 0.1-5. NSF Industry/University Cooperative Research Center for Net-Centric Software & Systems.
[09]	Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems (TOIS), 22 (1), 5-53.
[10]	Lemire, D. (2003). Cofi: A Java-Based Collaborative Filtering Library. Canada: National Research Council of Canada.
[11]	Lew, D., & Sowell, B. (2007). Carleton Recommender systems. (D. Musicant, Ed.) Northfield, Minnesota, USA: Carleton College.
[12]	Mahout-Team. (2013). Apache Mahout™. The Apache Software Foundation.
[13]	Microsoft, My Media PC, RichHanbidge, My Media Wp 2 Lead, My Media Wp 3 Lead, My Media Wp 4 Lead, et al. (2013). My Media Dynamic Personalization and Recommendation Software Framework Toolkit. MyMedia project funded through the EU Framework 7 Programme Networked Media initiative.
[14]	Oracle. (n. d.). Java language. (Oracle Corporation) Retrieved December 25, 2014, from Java website: https://www.oracle.com/java.
[15]	Smart-Agent-Technologies. (2013). easyrec. Research Studios Austria Forschungsgesellschaft mbH.
[16]	Telematica-Instituut. (2007, June 29). Duine Framework. Telematica Instituut/Novay.
[17]	Vogoo-Team, & DROUX, S. (2008). Vogoo PHP LIB. 2.2. Source Forge.
[18]	ECMA, "The JSON Data Interchange Format," ECMA International, Geneva, 2013.