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Effects of Product Recommendations on Customer Behavior in e-Commerce : An Empirical Analysis of Online Bookstore Clickstream Data  

Lee, Hong-Joo (가톨릭대학교 경영학부)
Publication Information
Journal of the Korean Operations Research and Management Science Society / v.33, no.3, 2008 , pp. 59-76 More about this Journal
Abstract
Studies of recommender systems have focused on improving their performance in terms of error rates between the actual and predicted preference values. Also, many studies have been conducted to investigate the relationships between customer information processing and the characteristics of recommender systems via surveys and web-based experiments. However, the actual impact of recommendation on product pages for customer browsing behavior and decision-making in the commercial environment has not, to the best of our knowledge, been investigated with actual clickstream data. The principal objective of this research is to assess the effects of product recommendation on customer behavior in e-Commerce, using actual clickstream data. For this purpose, we utilized an online bookstore's clickstream data prior to and after the web site renovation of the store. We compared the recommendation effects on customer behavior with the data. From these comparisons, we determined that the relevant recommendations in product pages have positive relationships with the acquisition of customer attention and elaboration. Additionally, the placing of recommended items in shopping cart is positively related to suggesting the relevant recommendations. However, the frequencies at which the recommended items were purchased did not differ prior to and after the renovation of the site.
Keywords
Product Recommendation; Customer Behavior; e-Commerce; Clickstream data;
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1 Awad, N.F. and M.S. Krishan, "The Personalization Privacy Paradox：An Empirical Evaluation of Information Transparancy and The Willingness to be Profiled Online for Personalization," MIS Quarterly, Vol. 30, No.1(2006), pp.13-28.   DOI
2 Balabanovic, M. and Y. Shoham, "Fab： Content-based, Collaborative recommendation," Communication of the ACM, Vol. 40, No.3(1997), pp.66-72.
3 Bucklin, R.E., J.M. Lattin, A. Ansari, S. Gupta, D. Bell, E. Coupley, J.D.C. Little, C. Mela, A. Montgomery, and J. Steckel, "Choice and the Internet：From Clickstream to Research Stream," Marketing Letters, Vol.13, No.3(2002), pp.245-258.   DOI   ScienceOn
4 Lee, J., M. Podlaseck, E. Schonberg, and R. Hoch, "Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising," Data Mining and Knowledge Discovery, Vol.5 (2001), pp.59-84.   DOI   ScienceOn
5 Liang, T.-P., H.-J. Lai, and Y.-C. Ku, "Personalized Content Recommendation and User Satisfaction：Theoretical Synthesis and Empirical Findings," Journal of Management Information Systems, Vol.23, No. 3(2007), pp.45-70.   DOI   ScienceOn
6 Linden, G., B. Smith, and J. York, "Amazon. com Recommendations：Item-to-item Collaborative Filtering," IEEE Internet Computing, Vol.7, No.3(2003), pp.76-80.   DOI   ScienceOn
7 Moe, W.W., "Buying, Searching, or Browsing： Differentiating Between Online Shoppers Using In-Store Navigational Clickstream," Journal of Consumer Psychology, Vol.13, No.1-2(2003), pp.29-39.   DOI
8 Montgomery, A.L., S. Li, K. Srinivasan, and J.C. Liechty, "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, Vol.23, No.4(2004), pp.579-595.   DOI   ScienceOn
9 Nakamoto, R., S. Nakajima, J. Miyazaki, and S. Uemura, "Tag-Based Contextual Collaborative Filtering," Proceedings of DEWS2007, Hiroshima, Japan, 2007, pp.25 -30.
10 Resnick, P. and H.R. Varian, "Recommender systems," Communications of the ACM, Vol.40, No.3(1997), pp.56-58
11 Herlocker, J.L., J.A. Konstan, L.G. Terveen, and J.T. Riedl, "Evaluating collaborative filtering recommender systems," ACM Transactions on Information Systems, Vol.22, No.1(2004), pp.5-53.   DOI   ScienceOn
12 Adomavicius, G. and A. Tuzhilin, "Personalization technologies：a process-oriented perspective," Communication of the ACM, Vol.48, No.10(2005), pp.83-90.
13 Breese, J.S., D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, USA, 1998.
14 Kim, B.M., Q. Li, C.S. Park, S.G. Kim, and J.Y. Kim, "A new approach for combining content-based and collaborative filters," Journal of Intelligent Information Systems, Vol.27, No.1(2006), pp.79-91.   DOI
15 Kim, C.Y., J.K. Lee, Y.H. Cho, and D.H. Kim, "Viscors：A Visual-Content Recommender for the Mobile Web," IEEE Intelligent Systems, Vol.19, No.6(2004), pp.32 -39.
16 Rojsattarat, E. and N. Soonthornphisaj, "Hybrid Recommendation：Combining Content- Based Prediction and Collaborative Filtering," Proceedings of IDEAL 2003, LNCS 2690, 2003, pp.337-344.
17 Ho, S.Y. and S.H. Kwok, "The Attraction of Personalized Service for Users in Mobile Commerce：An Empirical Study," ACM SIGecom Exchanges, Vol.3, No.4(2003), pp. 10-18.   DOI
18 Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens：an open architecture for collaborative filtering of netnews," Proceedings of Proceedings of the 1994 ACM conference on Computer supported cooperative work Chapel Hill, North Carolina, United States 1994, pp.175 -186.
19 Schafer, J.B., J. Konstan, and J. Riedl, "Recommender Systems in E-Commerce," Proceedings of E-Commerce, Denver, Colorado, 1999, ACM, pp.158-166.
20 Ansari, A., S. Essegaier, and R. Kohli, "Internet Recommendation Systems," Journal of Marketing Research, Vol.37, No.3 (2000), pp.363-375.   DOI   ScienceOn
21 Kim, J.K., Y.H. Cho, W.J. Kim, J.R. Kim, and J.H. Suh, "A personalized recommendation procedure for Internet shopping support," Electronic Commerce Research and Applications, Vol.1, No.3-4(2002), pp. 301-313.   DOI   ScienceOn
22 Cho, Y.B., Y.H. Cho, and S.H. Kim, "Mining changes in customer buying behavior for collaborative recommendations," Expert Systems with Applications, Vol.28, No.2(2005), pp.359-369.   DOI   ScienceOn
23 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, "Item-based collaborative filtering recommendation algorithms," Proceedings of Proceedings of the 10th international conference on World Wide Web, Hong Kong, 2001, pp.285-295.
24 Bodapati, A.V., "Recommendation Systems with Purchase Data," Journal of Marketing Research, Vol.14(2008), pp.77-93.
25 Kumar, N. and I. Benbasat, "The Influence of Recommendations and Consumer Reviews on Evaluations of Websites," Information Systems Research, Vol.17, No.4 (2006), pp.425-439.   DOI   ScienceOn
26 Awad, N.F., J.L. Jones, and J. Zhang, "Does Search Matter? Using Online Clickstream Data to Examine the Relationship Between Online Search and Purchase Behavior," Proceedings of Twenty-Seventh International Conference on Information Systems, Milwaukee, 2006, pp.915-926.
27 Kim, J.W., B.H. Lee, M.J. Shaw, H.L. Chang, and M. Nelson, "Application of decision- tree induction techniques to personalized advertisements on Internet storefronts," International Journal of Electronic Commerce, Vol.5, No.3(2001), pp.45-62.   DOI
28 Moe, W.W. and P.S. Fader, "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, Vol.50, No.3(2004), pp.326-335.   DOI   ScienceOn
29 Montgomery, A.L., "Applying Quantitative Marketing Techniques to the Internet," Interfaces, Vol.31, No.2(2001), pp.90-108.   DOI   ScienceOn
30 Tam, K.Y. and S.Y. Ho, "Web Personalization as a Persuasion Strategy：An Elaboration Likelihood Model Perspective," Information Systems Research, Vol.16, No. 3(2005), pp.271-291.   DOI   ScienceOn
31 Xiao, B. and I. Benbasat, "E-Commerce Product Recommendation Agents：Use, Characteristics, and Impact," MIS Quarterly, Vol.31, No.1(2007), pp.137-209.   DOI
32 Tam, K.Y. and S.Y. Ho, "Understanding The Impact of Web Personalization on User Information Processing and Decision Outcomes," MIS Quarterly, Vol.30, No.4 (2006), pp.865-890.   DOI
33 Claypool, M., A. Gokhale, T. Miranda, P. Murnikov, D. Netes, and M. Sartin, Combining Content-Based and Collaborative Filters in an Online Newspaper, Worcester Polytechnic Institute, 1999.
34 Konstan, J.A., B.N. Miller, D. Maltz, J. L. Herlocker, L.R. Gordon, and J. Riedl, "GroupLens：Applying collaborative filtering to USENET news," Communication of the ACM, Vol.40, No.3(1997), pp.77-87.   DOI   ScienceOn
35 Mobasher, B., H. Dai, T. Luo, and M. Nakagawa, "Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data," Proceedings of Workshop Intelligent Techniques for Web Personalization, IJCAI-2001, Seattle Washington, 2001, pp.53-60.
36 Swaminathan, V., "The Impact of Recommendation Agents on Consumer Evaluation and Choice：The Moderating Role of Category Risk, Product Complexity, and Consumer Knowledge," Journal of Consumer Psychology, Vol.13, No.1-2(2003), pp.93-101.   DOI
37 Moe, W.W. and P.S. Fader, "Capturing Evolving Visit Behavior in Clickstream Data," Journal of Interactive Marketing, Vol.18, No.1(2004), pp.5-19.
38 Schafer, J.B., J.A. Konstan, and J. Riedl, "E-Commerce Recommendation Applications," Data Mining and Knowledge Discovery, Vol.5, No.1-2(2001), pp.115-153.   DOI   ScienceOn
39 Park, Y.-H. and P.S. Fader, "Modeling Browsing Behavior at Multiple Websites," Marketing Science, Vol.23, No.3(2004), pp.280-303.   DOI   ScienceOn
40 Danaher, P.J., G.W. Mullarkey, and S. Essegaier, "Factors affecting Web site visit duration：A cross-domain analysis," Journal Of Marketing Research, Vol.43, No. 2(2006), pp.182-194.   DOI   ScienceOn
41 Mobasher, B., H. Dai, T. Luo, and M. Nakagawa, "Effective Personalization Based on Association Rule Discovery from Web Usage Data," Proceedings of WIDM 2001, Atlanta, GA, USA, 2001, pp.9-15.