Traditionally, direct marketing companies have relied on pre-testing to select the best offers to send to their audiences. Companies systematically dispatch the offers under consideration to a limited sample of potential buyers, rank them with respect to their performance and, based on this ranking, decide which offers to send to the wider population. Though this pre-testing process is simple and widely used, recently the direct marketing industry has been under increased pressure to further optimize learning, in particular when facing severe time and space constraints. Taking into account the multimedia nature of offers, which typically comprise both a visual and text component, we propose a two-phase learning strategy based on a cascade of regression methods. This proposed approach takes advantage of visual and text features to improve and accelerate the learning process. Experiments in the domain of a commercial multimedia messaging service show the effectiveness of the proposed methods that improve on classical learning techniques. The main contribution of the present work is to demonstrate that direct marketing firms can exploit the information on visual content to optimize the learning phase. The proposed methods can be used in any multimedia direct marketing domains in which offers are composed by image and text.
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By definition, a holistic cue is one that is processed over the entire human visual field and does not require attention to analyze local features 
Taking into account the overall simulation settings, 30 offers per day is an arrival rate value comparable to the mean arrival rate values observed in our real system.
Direct Marketing Association (2007) The power of direct marketing: ROI, sales, expenditures and employment in the U.S., 2006–2007 edition. Direct Marketing Association
Netsize (2007) Convergence: everything is going mobile. The Netsize Guide 2007. Netsize
Roberts M, Berger P (1989) Direct marketing management. Prentice-Hall, Englewood Cliffs
Prinzie A, Van Den Poel D (2005) Constrained optimization of data-mining problems to improve model performance: a direct-marketing application. Expert Syst Appl 29(3):630–640
Nash E (2000) Direct marketing. McFraw-Hill, New York
Cui G, Wong ML, Lui HK (2006) Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Manage Sci 52(4):597–612
Baesens B, Viaene S, Van den Poel D, Vanthienen J, Dedene G (2002) Bayesian neural network learning for repeat purchase modelling in direct marketing. Eur J Oper Res 127(1):191–211
Battiato S, Farinella G, Giuffrida G, Tribulato G (2007) Data mining learning bootstrap through semantic thumbnail analysis. In: SPIE-IS&T 19th annual symposium electronic imaging science and technology 2007—multimedia content access: algorithms and systems
Julesz B (1981) Textons, the elements of texture perception, and their interactions. Nature 290:91–97
Renninger LW, Malik J (2004) When is scene recognition just texture recognition? Vis Res 44:2301–2311
Varma M, Zisserman A (2005) A statistical approach to texture classification from single images. Int J Comput Vis 62(1–2):61–81
Winn J, Criminisi A, Minka T (2005) Object categorization by learned universal visual dictionary. In: ICCV ’05: Proceedings of the tenth IEEE international conference on computer vision, Washington, DC, USA, IEEE Computer Society. pp 1800–1807
Barnard K, Forsyth DA (2001) Learning the semantics of words and pictures. In: ICCV. pp 408–415
Naccari F, Battiato S, Bruna A, Capra A, Castorina A (2005) Natural scene classification for color enhancement. IEEE Trans Consumer Electron 5:234–239
Biederman I, Mezzanotte R, Rabinowitz J (1982) Scene perception: detecting and judging objects undergoing relational violations. 14:143–177
Biederman I (1987) Recognition by components: a theory of human image interpretation. Psychol Rev 94:115—148
Potter M (1975) Meaning in visual search. Science 187:965–966
Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42:145–175
Fei-Fei L, Perona P (2005) A bayesian hierarchical model for learning natural scene categories. In: IEEE conference on computer vision and pattern recognition
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE conference on computer vision and pattern recognition, vol II. pp 2169–2178
Bergen JR, Julesz B (1983) Rapid discrimination of visual patterns. IEEE Trans Syst Man Cybern 13:857–863
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575
Hull D (1996) Stemming algorithms: a case study for detailed evaluation. J Am Soc Inf Sci 47:70–84
Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth and Brooks, Monterey
Cleveland WS, Devlin SJ, Grosse E (1988) Regression by local fitting: methods, properties, and computational algorithms. J Econom 37(1):87–114
Shawe-Taylor J, Cristianini N (2000) Support vector machines and other kernel-based learning methods. Cambridge University Press, London
Taylor P, Caley R, Black AW, King S (1994–1999) Wagon, edinburgh speech tools library
Oren N (2002) Examining tf.idf based information retrieval with genetic programming. In: SAICSIT ’02: Proceedings of the 2002 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology, Republic of South Africa, South African Institute for Computer Scientists and Information Technologists. pp 224–234
Schölkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245
Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines
Perronnin F (2007) Universal and adapted vocabularies for generic visual categorization. Pattern Anal Mach Intell IEEE Trans 30(7):1243–1256
Oza NC (2005) Online bagging and boosting. In: Systems, man and cybernetics, 2005 IEEE international conference on. pp 2340–2345
The authors would like to thank Daniele Ravì for helping in the implementation of the simulation studies. The authors would also like to thank Neodata Group for giving access to the mobile messaging dataset, and for helping in the implementation and testing of the proposed approach.
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Battiato, S., Farinella, G.M., Giuffrida, G. et al. Exploiting visual and text features for direct marketing learning in time and space constrained domains. Pattern Anal Applic 13, 143–157 (2010). https://doi.org/10.1007/s10044-009-0145-2
- Visual and text features
- Learning in time and space constrained domains
- Multimedia messaging services
- Direct marketing