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AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS

Year 2017, Volume: 3 Issue: 2, 143 - 149, 24.12.2017
https://doi.org/10.22531/muglajsci.357313

Abstract

Because of the sparsity
problems in databases, fake accounts can easily affect results of recommender
algorithms especially when a product does not have enough votes by consumers.
Generally, fake accounts are created by the owner of the product in order to
raise their product score or by the ill-wishers who wants to denigrate a
product or a company. This situation represents a great sense for e-commerce
platforms especially when considering that majority of companies have less than
1% density of database. In order to overcome
negative effects of the fake accounts in e-commerce platforms, this study
proposes a recommender model, which will find the consumers who are trustful
and have a great effect on other’s opinion by analyzing the relationship
between consumers. With the proposed model, the Recommender Systems (RS) are expected to
provide recommendations to customers based on trustful users’ opinions to
improve the quality of RS in e-commerce platforms.

References

  • [1] M. Işık, H. Dağ and I. Yenidoğan, "E-ticaret Sistemleri İçin Bir Öneri Sistemi: Mahout," in YBS.2014, İstanbul, 2014.
  • [2] S. Deng, L. Huang ve G. Xu, «Social network-based service recommandation with trust enhancement,» [Çevrimiçi]. Available: http://www.sciencedirect.com/science/article/pii/S0957417414004102. [%1 tarihinde erişilmiştirAugust 2014].
  • [3] P. Winters and M. Zeller, "Social Media, Recommendation Engines and Real-Time Model Execution: A Practical Case Study," 2011. [Online]. Available: https://www.knime.org/files/knime_zementis_white_paper.pdf. [Accessed 10 August 2015].
  • [4] N. Tintarev, «Explaining recommendations,» Aberdeen, 2009.
  • [5] P. Melville and V. Sindhwani, "Recommender Systems," [Online]. Available: http://www.prem-melville.com/publications/recommender-systems-eml2010.pdf. [Accessed 21 August 2015].
  • [6] S. Alag, Collective Intelligence, Greenwich: Manning Publication Co., 2009.
  • [7] A. N. Langville and C. D. Meyer, Google's PageRank and Beyond: The Science of Search Engine Rankings, Princeton, New Jersey: Princeton University Press, 2006.
  • [8] R. S. Wills, When rank trumps precision: Using the power method to compute Google's PageRank, Raleigh: Nort Carolina State University, Dept. of Mathematics, 2007.
  • [9] S. Brin, L. Page, R. Motwami and T. Winogard, "The PageRank citation ranking: Bringing order to the Web," Stanford University, Computer Science Department, 1999.
  • [10] J. Chen, "Personalized Recommendation in Social Network Sites," Minnesota, 2011.
  • [11] M. Işık, Pagerank && Trustrank, İstanbul: İkinci Adam, 2013.
  • [12] M. Jamali and M. Ester, "TrustWalker: A Random walk model for combining trust-based and item-based recommendation," Proceedings of the 15th ACM SIGKDD international conference, p. 2009, June 28- July 1 2009.
  • [13] A. Jane, "Analyzing Big Data with Twitter: Recommender Systems," 9 September 2012. [Online]. Available: https://www.youtube.com/watch?v=NSscbT7JwxY. [Accessed 20 October 2016].
  • [14] S. Owen, R. Anil and E. Friedman, Mahout in Action, Shelter Island: Manning Publications Co., 2012.

E-TİCARET ORTAMLARI İÇİN ETKİLİ BİR TAVSİYE MODELİ

Year 2017, Volume: 3 Issue: 2, 143 - 149, 24.12.2017
https://doi.org/10.22531/muglajsci.357313

Abstract

Sahte kullanıcı hesapları, veri tabalarındaki seyreklik problemlerinden dolayı özellikle yeteri kadar kullanıcı tarafından puanlanmamış ürünlerde tavsiye algoritmalarını kolaylıkla etkileyebilmektedirler. Genellikle bu kullanıcı hesapları kendi ürününün puanını artırmak isteyen ürün sahipleri olabildiği gibi herhangi bir ürünü veya şirketi karalamak isteyen kötü niyetli kişiler de olabilmektedir. Bu durum birçok şirketin veri tabanı yoğunluğunun %1 den daha az olduğu düşünülürse e-ticaret ortamlarına nasıl bir etki yarattığı tahmin edilebilir. Bu çalışmada, sahte hesapların e-ticaret ortamlarında oluşturdukları negatif etkilerin üstesinden gelebilmek için, kullanıcılar arasındaki ilişkiler analiz edilerek diğer kullanıcılar üzerinde etkisi olan ve gerçekten güvenilir olduğu düşünülen kullanıcılar bulunarak bir tavsiye modeli oluşturulmaktadır. Böylece, güvenilir kullanıcıların düşüncelerinden yola çıkılarak e-ticaret ortamlarında kullanıcılara tavsiyelerde bulunan Tavsiye Sistemlerinin (TS) kalitesini artıracak bir tavsiye sistemi oluşturulacaktır.

References

  • [1] M. Işık, H. Dağ and I. Yenidoğan, "E-ticaret Sistemleri İçin Bir Öneri Sistemi: Mahout," in YBS.2014, İstanbul, 2014.
  • [2] S. Deng, L. Huang ve G. Xu, «Social network-based service recommandation with trust enhancement,» [Çevrimiçi]. Available: http://www.sciencedirect.com/science/article/pii/S0957417414004102. [%1 tarihinde erişilmiştirAugust 2014].
  • [3] P. Winters and M. Zeller, "Social Media, Recommendation Engines and Real-Time Model Execution: A Practical Case Study," 2011. [Online]. Available: https://www.knime.org/files/knime_zementis_white_paper.pdf. [Accessed 10 August 2015].
  • [4] N. Tintarev, «Explaining recommendations,» Aberdeen, 2009.
  • [5] P. Melville and V. Sindhwani, "Recommender Systems," [Online]. Available: http://www.prem-melville.com/publications/recommender-systems-eml2010.pdf. [Accessed 21 August 2015].
  • [6] S. Alag, Collective Intelligence, Greenwich: Manning Publication Co., 2009.
  • [7] A. N. Langville and C. D. Meyer, Google's PageRank and Beyond: The Science of Search Engine Rankings, Princeton, New Jersey: Princeton University Press, 2006.
  • [8] R. S. Wills, When rank trumps precision: Using the power method to compute Google's PageRank, Raleigh: Nort Carolina State University, Dept. of Mathematics, 2007.
  • [9] S. Brin, L. Page, R. Motwami and T. Winogard, "The PageRank citation ranking: Bringing order to the Web," Stanford University, Computer Science Department, 1999.
  • [10] J. Chen, "Personalized Recommendation in Social Network Sites," Minnesota, 2011.
  • [11] M. Işık, Pagerank && Trustrank, İstanbul: İkinci Adam, 2013.
  • [12] M. Jamali and M. Ester, "TrustWalker: A Random walk model for combining trust-based and item-based recommendation," Proceedings of the 15th ACM SIGKDD international conference, p. 2009, June 28- July 1 2009.
  • [13] A. Jane, "Analyzing Big Data with Twitter: Recommender Systems," 9 September 2012. [Online]. Available: https://www.youtube.com/watch?v=NSscbT7JwxY. [Accessed 20 October 2016].
  • [14] S. Owen, R. Anil and E. Friedman, Mahout in Action, Shelter Island: Manning Publications Co., 2012.
There are 14 citations in total.

Details

Subjects Engineering
Journal Section Computer Engineering
Authors

Muhittin Işık 0000-0001-6194-9074

Hasan Dağ 0000-0001-6252-1870

Publication Date December 24, 2017
Published in Issue Year 2017 Volume: 3 Issue: 2

Cite

APA Işık, M., & Dağ, H. (2017). AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS. Mugla Journal of Science and Technology, 3(2), 143-149. https://doi.org/10.22531/muglajsci.357313
AMA Işık M, Dağ H. AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS. Mugla Journal of Science and Technology. December 2017;3(2):143-149. doi:10.22531/muglajsci.357313
Chicago Işık, Muhittin, and Hasan Dağ. “AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS”. Mugla Journal of Science and Technology 3, no. 2 (December 2017): 143-49. https://doi.org/10.22531/muglajsci.357313.
EndNote Işık M, Dağ H (December 1, 2017) AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS. Mugla Journal of Science and Technology 3 2 143–149.
IEEE M. Işık and H. Dağ, “AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS”, Mugla Journal of Science and Technology, vol. 3, no. 2, pp. 143–149, 2017, doi: 10.22531/muglajsci.357313.
ISNAD Işık, Muhittin - Dağ, Hasan. “AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS”. Mugla Journal of Science and Technology 3/2 (December 2017), 143-149. https://doi.org/10.22531/muglajsci.357313.
JAMA Işık M, Dağ H. AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS. Mugla Journal of Science and Technology. 2017;3:143–149.
MLA Işık, Muhittin and Hasan Dağ. “AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS”. Mugla Journal of Science and Technology, vol. 3, no. 2, 2017, pp. 143-9, doi:10.22531/muglajsci.357313.
Vancouver Işık M, Dağ H. AN EFFECTIVE ROCOMMENDER MODEL FOR E-COMMERCE PLATFORMS. Mugla Journal of Science and Technology. 2017;3(2):143-9.

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