We propose an enhanced collaborative filtering
method using Hofstede-s cultural dimensions, calculated for 111
countries. We employ 4 of these dimensions, which are correlated to
the costumers- buying behavior, in order to detect users- preferences
for items. In addition, several advantages of this method
demonstrated for data sparseness and cold-start users, which are
important challenges in collaborative filtering. We present
experiments using a real dataset, Book Crossing Dataset.
Experimental results shows that the proposed algorithm provide
significant advantages in terms of improving recommendation
quality.
[1] Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. "Analysis of
recommendation algorithms for E-commerce". In Proceedings of the
Second ACM Conference on Electronic Commerce, 2000, 158-167.
[2] Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. "Item-based
collaborative filtering recommendation algorithms". In Proceedings of
the 10th International Conference on World Wide Web, 2001, 285-295.
[3] SuhT.W., KwonI.G., "Globalization and reluctant buyers", International
Marketing Review, 19 (6), 2002, pp. 663-680.
[4] Goethals, F. G., Carugati, A., and Leclercq, A. , "Differences in ecommerce
behavior between neighboring countries: the case of France
and Belgium". SIGMIS Database 40, 4, pp. 88-116, Oct. 2009.
[5] Kumar N., Benbasat I. "The influence of recommendations and
consumer reviews on evaluations of websites", Information Systems
Research, Vol. 17:4, 2006, pp. 425-439.
[6] Riegner C. "Word of Mouth on the Web: Theimpact of web 2.0 on
consumer purchase decisions", Journal of Advertising Research, pp.
436-447, December 2007.
[7] Fogg, B.J., Marshall J., Laraki O., et al. "What Makes Web Sites
Credible? A Report on a Large Quantitative Study", In Proc. CHI 2001
ACM Press, 2001 , pp. 61-68.
[8] Junjie Chu; Xiaohu Zhu; , "Culture-oriented Evaluation Method for
Interaction: Applied in Chinese E-commerce website design," Computer
Science and Information Technology (ICCSIT), 2010 3rd IEEE
International Conference on , vol.2, no., pp.331-335, 9-11 July 2010.
[9] Singh N., Furrer OK, Ostinelli M. "To localize or to standardize on the
web: empirical evidence from Italy, India, Netherlands, Spain, and
Switzerland", The Multinational Business Review, Vol. 12:1, 2004, pp.
69-87.
[10] Sinkovics R.R., Yamin M., Hossinger M. "Cultural adaptation in cross
border e-commerce: a study of German companies", Journal of
Electronic Commerce Research, Vol. 8:4, pp.221-235, 2007.
[11] L. Chen and P. Pu, "A cross-cultural user evaluation of product
recommender interfaces," presented at the Proceedings of the 2008
ACM conference on Recommender systems, Lausanne, Switzerland,
2008.
[12] Rodgers S., Harris M.A. "Gender and Ecommerce: An exploratory
study", Journal of Advertising Research, pp.322-329, September 2003.
[13] Seock Y., Bailey L.R. "The influence of college student-s shopping
orientations and gender differences on online information searches and
purchase behaviors" ,International journal of Consumer Studies, Vol.32,
pp. 113-121, 2008.
[14] Pavlou P.A., Chai L. "What drives electronic commerce across cultures?
A cross cultural empirical investigation of the theory of planned
behavior", Journal of Electronic Commerce Research, Vol. 3:4, pp. 240-
253, 2002.
[15] Singh, N., Fassott, G., Chao, M.C.H., and Hoffmann,J.A.
"Understanding International Website Usage: A Cross-National Study of
German, Brazilian, and Taiwanese Online Consumers", International
Marketing Review, Vol. 23:1, 2006 ,pp.83-.
[16] Lim K. H., Leung K., Sia C. L., and Lee M. K. O., "Is ecommerce
Boundary-Less? Effects of Individualism-Collectivism and Uncertainty
Avoidance on Internet Shopping," Journal of International Business
Studies, 35(6), 2004, pp. 545-559.
[17] McCoy S., Everard A., and Jones B. "An Examination of the
Technology Acceptance Model in Uruguay and the Us: A Focus on
Culture", Journal of Global Information Technology Management
Vol.8:2, pp. 27-46, 2005.
[18] DE MOOIJ, M., Consumer Behavior and Culture: Consequences for
Global Marketing and Advertising, Thousand Oaks, CA: Sage, 2004.
[1] Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. "Analysis of
recommendation algorithms for E-commerce". In Proceedings of the
Second ACM Conference on Electronic Commerce, 2000, 158-167.
[2] Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. "Item-based
collaborative filtering recommendation algorithms". In Proceedings of
the 10th International Conference on World Wide Web, 2001, 285-295.
[3] SuhT.W., KwonI.G., "Globalization and reluctant buyers", International
Marketing Review, 19 (6), 2002, pp. 663-680.
[4] Goethals, F. G., Carugati, A., and Leclercq, A. , "Differences in ecommerce
behavior between neighboring countries: the case of France
and Belgium". SIGMIS Database 40, 4, pp. 88-116, Oct. 2009.
[5] Kumar N., Benbasat I. "The influence of recommendations and
consumer reviews on evaluations of websites", Information Systems
Research, Vol. 17:4, 2006, pp. 425-439.
[6] Riegner C. "Word of Mouth on the Web: Theimpact of web 2.0 on
consumer purchase decisions", Journal of Advertising Research, pp.
436-447, December 2007.
[7] Fogg, B.J., Marshall J., Laraki O., et al. "What Makes Web Sites
Credible? A Report on a Large Quantitative Study", In Proc. CHI 2001
ACM Press, 2001 , pp. 61-68.
[8] Junjie Chu; Xiaohu Zhu; , "Culture-oriented Evaluation Method for
Interaction: Applied in Chinese E-commerce website design," Computer
Science and Information Technology (ICCSIT), 2010 3rd IEEE
International Conference on , vol.2, no., pp.331-335, 9-11 July 2010.
[9] Singh N., Furrer OK, Ostinelli M. "To localize or to standardize on the
web: empirical evidence from Italy, India, Netherlands, Spain, and
Switzerland", The Multinational Business Review, Vol. 12:1, 2004, pp.
69-87.
[10] Sinkovics R.R., Yamin M., Hossinger M. "Cultural adaptation in cross
border e-commerce: a study of German companies", Journal of
Electronic Commerce Research, Vol. 8:4, pp.221-235, 2007.
[11] L. Chen and P. Pu, "A cross-cultural user evaluation of product
recommender interfaces," presented at the Proceedings of the 2008
ACM conference on Recommender systems, Lausanne, Switzerland,
2008.
[12] Rodgers S., Harris M.A. "Gender and Ecommerce: An exploratory
study", Journal of Advertising Research, pp.322-329, September 2003.
[13] Seock Y., Bailey L.R. "The influence of college student-s shopping
orientations and gender differences on online information searches and
purchase behaviors" ,International journal of Consumer Studies, Vol.32,
pp. 113-121, 2008.
[14] Pavlou P.A., Chai L. "What drives electronic commerce across cultures?
A cross cultural empirical investigation of the theory of planned
behavior", Journal of Electronic Commerce Research, Vol. 3:4, pp. 240-
253, 2002.
[15] Singh, N., Fassott, G., Chao, M.C.H., and Hoffmann,J.A.
"Understanding International Website Usage: A Cross-National Study of
German, Brazilian, and Taiwanese Online Consumers", International
Marketing Review, Vol. 23:1, 2006 ,pp.83-.
[16] Lim K. H., Leung K., Sia C. L., and Lee M. K. O., "Is ecommerce
Boundary-Less? Effects of Individualism-Collectivism and Uncertainty
Avoidance on Internet Shopping," Journal of International Business
Studies, 35(6), 2004, pp. 545-559.
[17] McCoy S., Everard A., and Jones B. "An Examination of the
Technology Acceptance Model in Uruguay and the Us: A Focus on
Culture", Journal of Global Information Technology Management
Vol.8:2, pp. 27-46, 2005.
[18] DE MOOIJ, M., Consumer Behavior and Culture: Consequences for
Global Marketing and Advertising, Thousand Oaks, CA: Sage, 2004.
@article{"International Journal of Business, Human and Social Sciences:58524", author = "Mahboobe Zardosht and Nasser Ghasem-Aghaee", title = "Culturally Enhanced Collaborative Filtering", abstract = "We propose an enhanced collaborative filtering
method using Hofstede-s cultural dimensions, calculated for 111
countries. We employ 4 of these dimensions, which are correlated to
the costumers- buying behavior, in order to detect users- preferences
for items. In addition, several advantages of this method
demonstrated for data sparseness and cold-start users, which are
important challenges in collaborative filtering. We present
experiments using a real dataset, Book Crossing Dataset.
Experimental results shows that the proposed algorithm provide
significant advantages in terms of improving recommendation
quality.", keywords = "Collaborative filtering, Cross-cultural, E-commerce, Recommender systems", volume = "5", number = "11", pages = "1578-6", }
