Developing a Price-Sensitive Recommender System to Improve Accuracy and Business Performance of Ecommerce Applications

Panniello Umberto

《International Journal of Electronic Commerce Studies》 6卷1期 (2015/06) Pp. 1-18

https://doi.org/10.7903/ijecs.1348

並列摘要

Much work has been done on recommender systems (RS) and much evidence was collected from applications about their effectiveness on business. As a consequence, the use of RS has quickly shifted from information retrieval to automatic marketing tools. The main aim of marketing tools is to positively affect customers' purchasing decisions and we know through marketing literature that purchasing decisions are strongly influenced by price. However, few works have explored the issue of including price in a recommendation engine. In this paper, we want to describe the main issues of designing this type of price-sensitive recommendation engine. We want also to demonstrate what the effect is of this design on recommendations' accuracy and on business performance. We demonstrate that including price in an RS improves the accuracy of recommendations, but it has to be properly modeled in order to also improve business performance. We have experimented with a Price-Sensitive RS in a laboratory setting and compared it to a traditional one by varying several settings.

並列關鍵字

Recommender Systems ； Multidimensional ； Marketing ； Price Sensitivity

參考文獻

B. Schafer, J. Konstan, and J. Riedl, E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1), p115-153, 2001. http://dx.doi.org/10.1007/978-1-4615-1627-9_6.

M. Gorgoglione, U. Panniello, and A. Tuzhilin, The effect of context-aware recommendations on customer purchasing behavior and trust. Paper presented at the fifth ACM Conference on Recommender Systems, Chicago, October 23-27, 2011. http://dx.doi.org/10.1145/2043932.2043951.

T. Kamishima, and S. Akaho, Personalized pricing recommender system: multi-stage epsilon-greedy approach. Paper presented at the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, Chicago, IL, USA, October 23-27, 2011. http://dx.doi.org/10.1145/2039320.2039329.

J.B. Schafer, J. Konstan, and J. Riedi, Recommender systems in e-commerce. Paper presented at the 1st ACM conference on Electronic Commerce, Denver, CO, USA, November 3-5, 1999. http://dx.doi.org/10.1145/336992.337035.

G. Adomavicius, and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), p734-749, 2005. http://dx.doi.org/10.1109/TKDE.2005.99.