Recommender Systems as a Mobile Marketing Service

Donald J. Kridel, Daniel R. Dolk, David Castillo

Naval Postgraduate School, Monterey, USA.
University of Missouri-St. Louis, St. Louis, USA.
Voltari, Inc., New York, USA.

DOI: 10.4236/jssm.2013.65A004                

Abstract

Recommender systems (RSs) have become a familiar artifact in cyberspace as a vehicle for increasing revenues while deepening customer loyalty and satisfaction. Typically RS are developed in house by companies with a large product line and customer base. However, the structure of RS is often straightforward, and effective systems can be developed at relatively low cost, and thus offered as a marketing service. We discuss the anatomy of a specific recommender system designed for a telecommunication carrier which uses predictive analytics in the form of collaborative filtering techniques to recommend products to users. Collaborative filtering (CF) is based upon the premise that users who have purchased a particular product will have similar preferences to other users who also purchased the product. We discuss and compare three versions of a CF-based recommender system based upon customer purchase history, customer browsing history, and user segments respectively. The results in terms of increased sales suggest that RS offer substantial value as a mobile marketing service. We suggest ways in which RS can be generalized into RS generators for more rapid development and deployment. We then invert the RS perspective from product-centric to user-centric and suggest how this would work in customer targeting for mobile advertising campaigns. We conclude that RS in the largest sense are heavily model-feedback in nature and require increasingly sophisticated automated modeling and predictive analytics capabilities layered on a scalable big data infrastructure.

Keywords

Marketing Service; Predictive Analytics; Recommender Systems; Collaborative Filtering; Similarity Measures; Euclidean Distance; Cosine Distance; Pearson Correlation; Recommender System Generator; Adaptive Modeling; Customer Targeting

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Y. Koren, R. Bell and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” IEEE Computer, Vol. 42, No. 6, 2009, pp. 42-49.
[2]	F. Ricci, L. Rokach, B. Shapira and P. B. Kantor, “Introduction to Recommender Systems Handbook,” In: F. Ricci, L. Rokach, B. Shapira and P. B. Kantor, Recommender Systems Handbook, Springer, Berlin, 2011, pp. 1-35. http://dx.doi.org/10.1007/978-0-387-85820-3_1
[3]	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, Vol. 17, No. 6, 2005, pp. 734-749. http://dx.doi.org/10.1109/TKDE.2005.99
[4]	D. Jannach, M. Zanker, A. Felfernig and G. Friedrich. “Recommender Systems: An Introduction,” Cambridge University Press, Cambridge, 2010. http://dx.doi.org/10.1017/CBO9780511763113
[5]	P. Melville and V. Sindhwani, “Recommender Systems,” In: C. Sammut and G. Webb, Eds., Encyclopedia of Machine Learning, Springer, Berlin, 2010, pp. 829-838.
[6]	P. Resnick and H. Varian, “Recommender Systems,” Communications of the ACM, Vol. 40, No. 3, 1997, pp. 56-58. http://dx.doi.org/10.1145/245108.245121
[7]	D. Kridel and D. Dolk, “Automated Self-Service Modeling: Predictive Analytics as a Service,” Information Systems for e_Business Management, Vol. 11, No. 1, 2013, pp. 119-140.
[8]	J. L. Herlocker, 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. http://dx.doi.org/10.1145/963770.963772
[9]	T. L. Saaty, “Decision Making with the Analytic Hierarchy Process,” International Journal of Services Science, Vol. 1, No. 1, 2008, pp. 83-98. http://dx.doi.org/10.1504/IJSSCI.2008.017590
[10]	H. Kantz, B. Selman and M. Shah, “Referral Web: Combining Social Networks and Collaborative Filtering,” Communications of the ACM, Vol. 40, No. 3, 1997.
[11]	K. Li and T. C. Du, “Building a Targeted Mobile Advertising System for Location-Based Services,” Decision Support Systems, Vol. 54, No. 1, 2012, pp. 1-8. http://dx.doi.org/10.1016/j.dss.2012.02.002
[12]	L. Candillier, F. Meyer and M. Boullé, “Comparing State-of-the-Art Collaborative Filtering Systems,” Proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, 18-20 July 2007, pp. 548-562.
[13]	X. Su and T. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, Vol. 2009, Article ID: 4214225, 19 Pages.
[14]	B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” WWW ’01: Proceedings of the 10th International Conference on World Wide Web, New York, 1-5 May 2001, pp. 285-295.
[15]	K. Tso and L. Schmidt-Thieme, “Empirical Analysis of Attribute-Aware Recommender System Algorithms Using Synthetic Data,” Journal of Computers, Vol. 1, No. 4, 2006, pp. 18-29.
[16]	P. Rossi, R. McCulloch and G. Allenby, “The Value of Purchase History Data in Target Marketing,” Marketing Science, Vol. 15, No. 4, 1996, pp. 321-340. http://dx.doi.org/10.1287/mksc.15.4.321
[17]	E. Edmiston and D. Kridel, “Automated Modeling and Sales Targeting: Case Study for AT&T Advertising and Publishing,” Proceedings of DMA07 (Direct Marketing Association Conference and Exhibit), Chicago, 17 October 2007.
[18]	S. T. Yuan and Y. W. Tsao, “A Recommendation Mechanism for Contextualized Mobile Advertising,” Expert Systems with Applications, Vol. 24, No. 4, 2003, pp. 399-414. http://dx.doi.org/10.1016/S0957-4174(02)00189-6
[19]	G. Shaffer and Z. Zhang, “Competitive Coupon Targeting,” Marketing Science, Vol. 14, No. 4, 1995, 395-416. http://dx.doi.org/10.1287/mksc.14.4.395
[20]	M. D. Ekstrand, M. Ludwig, J. A. Konstan and J. T. Riedl, “Rethinking the Recommender Research Ecosystem: Reproducibility, Openness, and LensKit,” Proceedings of the 5th ACM Conference on Recommender Systems, Chicago, 23-27 October 2011, pp. 133-140.