Abstract
Most of the work in the recommendation system literature has been developed under the assumption that user preference has a static pattern. However, a consumer may be at different psychological stages in the marketing funnel and her product preferences may change several times before she makes her final purchase. In this paper, we build a dynamic Bayesian network to model the generative process of consumers’ browsing behaviors over time periods, considering both the conversion patterns among different psychological stages and preferences. In the prediction process, we create a ranked recommendation list of products for a new consumer to browse for her current period based on her browsing behaviors in all past periods and her predicted distribution of psychological stages and preferences in the current period. Thus, the multi-stage recommendation process gradually moves an e-commerce website consumer through the buying process, eventually converting her from a browser to a buyer.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Howard, J.A., Sheth, J.N.: The Theory of Buyer Behavior, vol. 14. Wiley, New York (1969)
Elzinga, D., Mulder, S., Vetvik, O.J., et al.: The consumer decision journey. McKinsey Q. 3, 96–107 (2009)
Mulpuru, S.: The purchase path of online buyers. Forrester report (2011)
Montgomery, A.L., Li, S., Srinivasan, K., Liechty, J.C.: Modeling online browsing and path analysis using clickstream data. Mark. Sci. 23(4), 579–595 (2004)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: WWW, pp. 285–295. ACM (2001)
Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Int. Comput. 7(1), 76–80 (2003)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Shani, G., Brafman, R.I., Heckerman, D.: An MDP-based recommender system. In: UAI, pp. 453–460. Morgan Kaufmann Publishers Inc. (2002)
Tsymbal, A.: The problem of concept drift: definitions and related work. Comput. Sci. Dept Trinity Coll. Dublin 106(2) (2004)
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)
Widmer, G.: Combining robustness and flexibility in learning drifting concepts. In: ECAI, pp. 468–472. PITMAN (1994)
Klinkenberg, R.: Learning drifting concepts: example selection vs. example weighting. Intell. Data Anal. 8(3), 281–300 (2004)
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53(4), 89–97 (2010)
Street, W.N., Kim, Y.: A streaming ensemble algorithm (SEA) for large-scale classification. In: ACM SIGKDD, pp. 377–382. ACM (2001)
Kolter, J.Z., Maloof, M.A.: Dynamic weighted majority: a new ensemble method for tracking concept drift. In: ICDM 2003, pp. 123–130. IEEE (2003)
Sahoo, N., Singh, P.V., Mukhopadhyay, T.: A hidden markov model for collaborative filtering. Manag. Inf. Syst. Q. 36, 1329–1356 (2012)
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: ICIUI, pp. 127–134. ACM (2002)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52. AUAI Press (1998)
Dunlavy, D.M., Kolda, T.G., Acar, E.: Temporal link prediction using matrix and tensor factorizations. ACM TKDD 5(2), 10 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Jiang, W., Wei, Q., Chen, G. (2018). A Dynamic Bayesian Network Based Collaborative Filtering Model for Multi-stage Recommendation. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-66824-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-66824-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66823-9
Online ISBN: 978-3-319-66824-6
eBook Packages: EngineeringEngineering (R0)