Öz
It is very difficult to estimate consumer behavior
due to different variables. There are also differences between the online
consumer and the traditional ones. While there are studies for the prediction
of purchasing behavior of online consumers, there is need for further studies
with larger data including different features. Large data is difficult to
obtain due to restrictions on private information and causes the analysis
systems run for a long time. So, in this study, it is aimed to create a
meaningful rule by estimating the purchasing behavior of online consumers with
fewer data. After selecting the Fisher Score feature in a current and open
database, training and test data were determined with K fold and a rule was
created with Decision Tree. As a result, it can be suggested that it is
possible to determine the purchasing behavior of online consumers with high
accuracy by using a single feature.
Anahtar Kelimeler
Online Purchasing Intention Artificial Intelligence Fisher Feature Score Selection Decision Tree K-Fold
Kaynakça
- Atsalakis, G. S., Atsalaki, I. G. ve Zopounidis, C. (2018). Forecasting the success of a new tourism service by a neuro-fuzzy technique. European Journal of Operational Research, 268(2), 716–727. doi:10.1016/J.EJOR.2018.01.044
- Bolón-Canedo, V., Sánchez-Marono, N., Alonso-Betanzos, A., Ben\’\itez, J. M. ve Herrera, F. (2014). A review of microarray datasets and applied feature selection methods. Information Sciences, 282, 111–135.
- Budak, H. (2018). Özellik Seçim Yöntemleri ve Yeni Bir Yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(Özel Sayı), 21–31. doi:10.19113/sdufbed.01653
- Dutta, S., Shekhar, S. ve Wong, W. Y. (1994). Decision support in non-conservative domains: Generalization with neural networks. Decision Support Systems, 11(5), 527–544. doi:10.1016/0167-9236(94)90023-X
- Gordini, N., Sanpaolo, I. ve Veglio, V. (2015). Customer relationship management and data mining : A classification decision tree to predict customer purchasing behavior in global market. doi:10.4018/978-1-4666-4450-2.ch001
- Gu, Q., Li, Z. ve Han, J. (2012). Generalized fisher score for feature selection. arXiv preprint arXiv:1202.3725.
- Gupta, R. ve Pathak, C. (2014). A Machine Learning Framework for Predicting Purchase by online customers based on Dynamic Pricing. Procedia - Procedia Computer Science, 36, 599–605. doi:10.1016/j.procs.2014.09.060
- Guyon, I. ve Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157–1182.
- Hoffman, D. L. ve Novak, T. P. (1996). Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations. Journal of Marketing, 60(3), 50. doi:10.2307/1251841
- How Page Value is calculated. (2019).Google. 12 Mart 2019 tarihinde https://support.google.com/analytics/answer/2695658?hl=en adresinden erişildi.
- Huerta, E. B., Duval, B. ve Hao, J.-K. (2010). A hybrid LDA and genetic algorithm for gene selection and classification of microarray data. Neurocomputing, 73(13–15), 2375–2383.
- King, M. A., Abrahams, A. S. ve Ragsdale, C. T. (2014). Ensemble methods for advanced skier days prediction. Expert Systems with Applications, 41(4 PART 1), 1176–1188. doi:10.1016/j.eswa.2013.08.002
- Kohavi, R. (1995). The Power of Decision Tables. ECML.
- Lee, S., Lee, S. ve Park, Y. (2007). A prediction model for success of services in e-commerce using decision tree : E-customer ’ s attitude towards online service, 33, 572–581. doi:10.1016/j.eswa.2006.06.005
- Nam, K. ve Schaefer, T. (1995). Forecasting international airline passenger traffic using neural networks. The Logistics and Transportation Review, 31(3), 239–252.
- Nielsen. (2013). Under The Influence: Consumer Trust in Advertising. www.nielsen.com/us/en/insights/news/2013/under-the-influence-consumer-trust-in-advertising.html adresinden erişildi.
- Park, S. ve Huh, S. (2019). A Social Network-Based Inference Model for Validating Customer Profile Data, 36(4), 1217–1237.
- Qiu, J., Lin, Z. ve Li, Y. (2015). Predicting customer purchase behavior in the e-commerce context. Electronic Commerce Research, 15(4), 427–452. doi:10.1007/s10660-015-9191-6
- Reha Alpar. (2016). Spor, Sağlık ve Eğitim Bilimlerinden Örneklerle Uygulamalı İstatistik ve Geçerlik - Güvenirlik. Detay Yayıncılık. http://www.kitapyurdu.com/index.php?route=product/product&product_id=308595&gclid=EAIaIQobChMIzpPGzdet2QIVTrHtCh1CKQoOEAQYASABEgLTLfD_BwE adresinden erişildi.
- Sakar, C. O., Polat, S. O., Katircioglu, M. ve Kastro, Y. (2018). Real-time prediction of online shoppers ’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 0. doi:10.1007/s00521-018-3523-0
- Ucar, M. K. ve Topal, I. (2018). Rule-Based Determination of Chinese tourists to Turkey Opt Profile. 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) içinde (ss. 1–4). IEEE. doi:10.1109/ASYU.2018.8553998
- Vellido, A., Lisboa, P. J. G. ve Meehan, K. (2015). Quantitative Characterization and Prediction of On-Line Purchasing Behavior : A Latent Variable Approach Approach, 4415. doi:10.1080/10864415.2000.11518380
Öz
Tüketici davranışlarını tahmin etmek çok fazla değişkene
bağlı olması nedeniyle oldukça zordur. Aynı zamanda çevrimiçi tüketiciyle
geleneksel tüketici arasında farklılıklar bulunmaktadır. Online tüketicilerin
satın alma davranışını tahmine yönelik bir süredir çalışmalar olmakla birlikte
çok sayıda özelliğe sahip büyük verilere ihtiyaç duyulmaktadır. Büyük
verilerin, kişisel bilgilere yönelik kısıtlamalar nedeniyle elde edilmesi zor
olmakta ve analiz sistemlerini uzun süre çalışmasına sebep olmaktadır. Bu
bağlamda, çalışmada online tüketicilerin satın alma davranışını daha az veriyle
tahmin ederek anlamlı bir kural oluşturmak amaçlanmıştır. Güncel ve açık bir
veri tabanında Fisher skor özellik seçme yapıldıktan sonra K fold ile eğitim ve
test verileri belirlenerek karar ağacı ile kural oluşturulmuştur. Sonuç olarak
tek bir özellik kullanılarak çevrimiçi tüketicinin satın alma davranışının
yüksek doğruluk oranıyla tespitinin mümkün olduğu görülmüştür.
Anahtar Kelimeler
Çevrimiçi Satın Alma Niyeti Yapay Zeka Fisher Skor Özellik Seçme Karar Ağacı K-Katlamalı
Kaynakça
- Atsalakis, G. S., Atsalaki, I. G. ve Zopounidis, C. (2018). Forecasting the success of a new tourism service by a neuro-fuzzy technique. European Journal of Operational Research, 268(2), 716–727. doi:10.1016/J.EJOR.2018.01.044
- Bolón-Canedo, V., Sánchez-Marono, N., Alonso-Betanzos, A., Ben\’\itez, J. M. ve Herrera, F. (2014). A review of microarray datasets and applied feature selection methods. Information Sciences, 282, 111–135.
- Budak, H. (2018). Özellik Seçim Yöntemleri ve Yeni Bir Yaklaşım. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(Özel Sayı), 21–31. doi:10.19113/sdufbed.01653
- Dutta, S., Shekhar, S. ve Wong, W. Y. (1994). Decision support in non-conservative domains: Generalization with neural networks. Decision Support Systems, 11(5), 527–544. doi:10.1016/0167-9236(94)90023-X
- Gordini, N., Sanpaolo, I. ve Veglio, V. (2015). Customer relationship management and data mining : A classification decision tree to predict customer purchasing behavior in global market. doi:10.4018/978-1-4666-4450-2.ch001
- Gu, Q., Li, Z. ve Han, J. (2012). Generalized fisher score for feature selection. arXiv preprint arXiv:1202.3725.
- Gupta, R. ve Pathak, C. (2014). A Machine Learning Framework for Predicting Purchase by online customers based on Dynamic Pricing. Procedia - Procedia Computer Science, 36, 599–605. doi:10.1016/j.procs.2014.09.060
- Guyon, I. ve Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157–1182.
- Hoffman, D. L. ve Novak, T. P. (1996). Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations. Journal of Marketing, 60(3), 50. doi:10.2307/1251841
- How Page Value is calculated. (2019).Google. 12 Mart 2019 tarihinde https://support.google.com/analytics/answer/2695658?hl=en adresinden erişildi.
- Huerta, E. B., Duval, B. ve Hao, J.-K. (2010). A hybrid LDA and genetic algorithm for gene selection and classification of microarray data. Neurocomputing, 73(13–15), 2375–2383.
- King, M. A., Abrahams, A. S. ve Ragsdale, C. T. (2014). Ensemble methods for advanced skier days prediction. Expert Systems with Applications, 41(4 PART 1), 1176–1188. doi:10.1016/j.eswa.2013.08.002
- Kohavi, R. (1995). The Power of Decision Tables. ECML.
- Lee, S., Lee, S. ve Park, Y. (2007). A prediction model for success of services in e-commerce using decision tree : E-customer ’ s attitude towards online service, 33, 572–581. doi:10.1016/j.eswa.2006.06.005
- Nam, K. ve Schaefer, T. (1995). Forecasting international airline passenger traffic using neural networks. The Logistics and Transportation Review, 31(3), 239–252.
- Nielsen. (2013). Under The Influence: Consumer Trust in Advertising. www.nielsen.com/us/en/insights/news/2013/under-the-influence-consumer-trust-in-advertising.html adresinden erişildi.
- Park, S. ve Huh, S. (2019). A Social Network-Based Inference Model for Validating Customer Profile Data, 36(4), 1217–1237.
- Qiu, J., Lin, Z. ve Li, Y. (2015). Predicting customer purchase behavior in the e-commerce context. Electronic Commerce Research, 15(4), 427–452. doi:10.1007/s10660-015-9191-6
- Reha Alpar. (2016). Spor, Sağlık ve Eğitim Bilimlerinden Örneklerle Uygulamalı İstatistik ve Geçerlik - Güvenirlik. Detay Yayıncılık. http://www.kitapyurdu.com/index.php?route=product/product&product_id=308595&gclid=EAIaIQobChMIzpPGzdet2QIVTrHtCh1CKQoOEAQYASABEgLTLfD_BwE adresinden erişildi.
- Sakar, C. O., Polat, S. O., Katircioglu, M. ve Kastro, Y. (2018). Real-time prediction of online shoppers ’ purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Computing and Applications, 0. doi:10.1007/s00521-018-3523-0
- Ucar, M. K. ve Topal, I. (2018). Rule-Based Determination of Chinese tourists to Turkey Opt Profile. 2018 Innovations in Intelligent Systems and Applications Conference (ASYU) içinde (ss. 1–4). IEEE. doi:10.1109/ASYU.2018.8553998
- Vellido, A., Lisboa, P. J. G. ve Meehan, K. (2015). Quantitative Characterization and Prediction of On-Line Purchasing Behavior : A Latent Variable Approach Approach, 4415. doi:10.1080/10864415.2000.11518380
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Aralık 2019 |
| Yayımlandığı Sayı | Yıl 2019 |