Behavior-aware user response modeling in social media: Learning from diverse heterogeneous data

European Journal of Operational Research

Volumn 241, Issue 2, 2015, Pages 422-434

  Chen, Zhen Yu ^a   Fan, Zhi Ping ^a,b   Sun, Minghe ^c  

^a NORTHEASTERN UNIVERSITY   (China)

^b STATE KEY LABORATORY OF SYNTHETICAL AUTOMATION FOR PROCESS INDUSTRIES   (China)

^c UNIVERSITY OF TEXAS AT SAN ANTONIO   (United States)

Author keywords

Data mining; Direct marketing; Engagement behavior; Response modeling; Social media

Indexed keywords

BEHAVIORAL RESEARCH; FEATURE EXTRACTION; LARGE DATASET; LEARNING SYSTEMS; METADATA; SALES; SOCIAL NETWORKING (ONLINE); SUPPORT VECTOR MACHINES;

COMPUTATIONAL EXPERIMENT; COMPUTATIONAL RESULTS; DIRECT MARKETING; ENGAGEMENT BEHAVIOR; HIERARCHICAL ENSEMBLE; PREDICTION PERFORMANCE; RESPONSE MODEL; SOCIAL MEDIA;

DATA MINING;

EID: 84911003647     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2014.09.008     Document Type: Article

References (49)

1. Baecke P.; Van den Poel D. Data augmentation by predicting spending pleasure using commercially available external data Journal of Intelligent Information Systems 36 3 2011 367 383
2. Baesens B.; Viaene S.; Van den Poel D.; Vanthienen J.; Dedene G. Bayesian neural network learning for repeat purchase modelling in direct marketing European Journal of Operational Research 138 1 2002 191 211
3. Ballings M.; Van den Poel D. Customer event history for churn prediction: How long is long enough? Expert Systems with Applications 39 18 2012 13517 13522
4. Bijmolt T.H.A.; Leeflang P.S.H.; Block F.; Eisenbeiss M.; Hardie B.G.S.; Lemmens A. et al. Analytics for customer engagement Journal of Service Research 13 3 2010 341 356
5. Bose I.; Chen X. Quantitative models for direct marketing: A review from systems perspective European Journal of Operational Research 195 1 2009 1 16
6. Buckinx W.; Moons E.; Van den Poel D.; Wets G. Customer-adapted coupon targeting using feature selection Expert Systems with Applications 26 4 2004 509 518
7. Burez J.; Van den Poel D. Handling class imbalance in customer churn prediction Expert Systems with Applications 36 3 2009 4626 4636
8. Cao L.; Ou Y.; Yu P.S. Coupled behavior analysis with applications IEEE Transactions on Knowledge and Data Engineering 24 8 2012 1378 1392
9. Chau M.; Xu J.J. Business intelligence in blogs: Understanding consumer interactions and communities MIS Quarterly 36 4 2012 1189 1216
10. Chen T.; Tang L.; Liu Q.; Yang D.; Xie S.; Cao X. et al. Combining factori-zation model and additive forest for collaborative followee recommendation 2012 KDD-Cup Workshop Beijing, China https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/SJTU.pdf
11. Chen W.-C.; Hsu C.-C.; Hsu J.-N. Optimal selection of potential customer range through the union sequential pattern by using a response model Expert Systems with Applications 38 6 2011 7451 7461
12. Chen Y.; Liu Z.; Ji D.; Xin Y.; Wang W.; Yao L. et al. Context-aware ensemble of multifaceted factorization models for recommendation prediction in social networks 2012 KDD-Cup Workshop Beijing, China https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/Shanda3.pdf
13. Chen Z.Y.; Fan Z.P.; Sun M. A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data European Journal of Operational Research 223 2 2012 461 472
14. Cheung C.M.K.; Thadani D.R. The impact of electronic word-of-mouth communication: A literature analysis and integrative model Decision Support Systems 54 1 2012 461 470
15. Cho Y.B.; Cho Y.H.; Kim S.H. Mining changes in customer buying behavior for collaborative recommendations Expert Systems with Applications 28 2 2005 359 369
16. Choi K.; Yoo D.; Kim G.; Suh Y. A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis Electronic Commerce Research and Applications 11 4 2012 309 317
17. Crone S.F.; Lessmann S.; Stahlbock R. The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing European Journal of Operational Research 173 3 2006 781 800
18. Cui G.; Wong M.L.; Lui H.-K. Machine learning for direct marketing response models: Bayesian networks with evolutionary programming Management Science 52 4 2006 597 612
19. Cui G.; Wong M.L.; Zhang G. Bayesian variable selection for binary response models and direct marketing forecasting Expert Systems with Applications 37 12 2010 7656 7662
20. Dellarocas C. The digitization of word of mouth: Promise and challenges of online feedback mechanisms Management Science 49 10 2003 1407 1424
21. Ha K.; Cho S.; MacLachlan D. Response models based on bagging neural networks Journal of Interactive Marketing 19 1 2005 17 30
22. Hill S.; Provost F.; Volinsky C. Network-based marketing: Identifying likely adopters via consumer networks Statistical Science 21 2 2006 256 276
23. Huang C.-L.; Huang W.-L. Handling sequential pattern decay: Developing a two-stage collaborative recommender system Electronic Commerce Research and Applications 8 3 2009 117 129
24. Kang P.; Cho S.; MacLachlan D.L. Improved response modeling based on clustering, under-sampling, and ensemble Expert Systems with Applications 39 8 2012 6738 6753
25. Kaplan A.M.; Haenlein M. Users of the world, unite! the challenges and opportunities of social media Business Horizons 53 1 2010 59 68
26. Kim Y.; Street W.N.; Russell G.J.; Menczer F. Customer targeting: A neural network approach guided by genetic algorithms Management Science 51 2 2005 264 276
27. Kolda T.; Bader B. Tensor decompositions and applications SIAM Review 51 3 2009 455 500
28. Lahbib D.; Boulle M.; Laurent D. Supervised pre-processing of numerical variables for multi-relational data mining Studies in Computational Intelligence 527 2014 95 109
29. Lee H.; Shin H.; Hwang S.; Cho S.; MacLachlan D. Semi-supervised response modeling Journal of Interactive Marketing 24 1 2010 42 54
30. Lessmann S.; Voß S. Supervised classification for decision support in customer relationship management Bortfeldt A. Homberger J. Kopfer H. Pankratz G. Stangmeier R. Intelligent decision support 2008 Gabler Wiesbaden 231 253
31. Lessmann S.; Voß S. A reference model for customer-centric data mining with support vector machines European Journal of Operational Research 199 2 2009 520 530
32. Li Y.-M.; Li T.-Y. Deriving market intelligence from Microblogs Decision Support Systems 55 1 2013 206 217
33. Liu D.-R.; Lai C.-H.; Lee W.-J. A hybrid of sequential rules and collaborative filtering for product recommendation Information Sciences 179 20 2009 3505 3519
34. Macskassy S.A.; Provost F. Classification in networked data: A toolkit and a univariate case study Journal of Machine Learning Research 8 2007 935 983
35. Mangold W.G.; Faulds D.J. Social media: The new hybrid element of the promotion mix Business Horizons 52 4 2009 357 365
36. Min S.H.; Han I. Detection of the customer time-variant pattern for improving recommender systems Expert Systems with Applications 28 2 2005 189 199
37. Paleologo G.; Elisseeff A.; Antonini G. Subagging for credit scoring models European Journal of Operational Research 201 2 2010 490 499
38. Park Y.-J.; Chang K.-N. Individual and group behavior-based customer profile model for personalized product recommendation Expert Systems with Applications 36 2 2009 1932 1939
39. Polikar P. Ensemble based systems in decision making IEEE Circuits and System Magazine 6 3 2006 21 45
40. Power D.J.; Phillips-Wren G. Impact of social media and Web 2.0 on decision-making Journal of Decision Systems 20 3 2011 249 261
41. Prinzie A.; Van den Poel D. Investigating purchasing sequence patterns for financial services using Markov, MTD and MTDg models European Journal of Operational Research 170 3 2006 710 734
42. Prinzie A.; Van den Poel D. Predicting home-appliance acquisition sequences: Markov/Markov for discrimination and survival analysis for modelling sequential information in NPTB models Decision Support Systems 44 1 2007 28 45
43. Prinzie A.; Van den Poel D. Modeling complex longitudinal consumer behavior with dynamic Bayesian networks: An acquisition pattern analysis application Journal of Intelligent Information System 36 3 2011 283 304
44. Sarawagi S. Information extraction Foundations and Trends in Databases 1 2007 261 377
45. van Doorn J.; Lemon K.; Mittal V.; Nass S.; Pick D.; Pimer P. et al. Customer engagement behavior: Theoretical foundations and research directions Journal of Service Research 13 3 2010 253 266
46. Vapnik V.N. Statistic learning theory 1998 Wiley New York
47. Verbeke W.; Dejaeger K.; Martens D.; Hur J.; Baesens B. New insights into churn prediction in the telecommunication sector: A profit driven data mining approach European Journal of Operational Research 218 1 2012 211 229
48. Zhang X.; Zhu J.; Xu S.; Wan Y. Predicting customer churn through interpersonal influence Knowledge-Based Systems 28 2012 97 104
49. Zhao X. Scorecard with latent factor models for user follow prediction problem 2012 KDD-Cup Workshop Beijing, China https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/FICO.pdf