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2018 | OriginalPaper | Buchkapitel

Identifying Price Sensitive Customers in E-commerce Platforms for Recommender Systems

verfasst von : Yingwai Shiu, Cheng Guo, Min Zhang, Yiqun Liu, Shaoping Ma

Erschienen in: Information Retrieval

Verlag: Springer International Publishing

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Abstract

With the rise of E-commerce platforms, more people are getting used to make their daily purchases in online stores, especially for price-discounted goods. Therefore, online price cutting campaigns become a common approach for online retailers to compete with other competitors. Still, giving the same price discount to all may not be an efficient resource allocation as different users respond differently due to the differences of their price sensitivities. So if we are able to identify price sensitive users, both sellers and recommender systems will be greatly benefited from it in terms of improved user targeting and item suggestions. However, due to lack of detailed historical price and customer profile data, it is challenging to conduct price sensitivity analysis via traditional economics approach. More importantly, it is really hard and costly for companies to acquire price sensitivity labeled data. To overcome the constraints, making use of rich meta data (e.g. comment reviews) and time stamp becomes an alternative way. Inspired by distinct expressive power of graphical model, especially bipartite graph, we propose a User Behaviour Probability Transition Model (UBPT) which considers both user and item price sensitivities as weightings in the probability transition process. First, we define our own set of price sensitive users according to anonymous user after-purchase reviews. Second, we integrate selected behavioral features via doing user and item encoding. Third, using both user and item similarities, we combine our algorithm to simulate the probability transition process. With the data set from JD.com, our proposed model significantly outperforms other baselines in most cases. Besides, through applying the idea of UBPT to recommender systems, we can also enhance the performance of traditional recommendation algorithms.

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Literatur
1.
Zurück zum Zitat Mercy, O.: Price flexibility in relation to consumer purchasing behaviour on-line (business to consumer electronic commerce) (2009) Mercy, O.: Price flexibility in relation to consumer purchasing behaviour on-line (business to consumer electronic commerce) (2009)
2.
Zurück zum Zitat Ghose, A., Sundararajan, A.: Evaluating pricing strategy using E-commerce data: evidence and estimation challenges. Stat. Sci. 131–142 (2006)MathSciNetCrossRef Ghose, A., Sundararajan, A.: Evaluating pricing strategy using E-commerce data: evidence and estimation challenges. Stat. Sci. 131–142 (2006)MathSciNetCrossRef
3.
Zurück zum Zitat Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in E-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158–166. ACM, November 1999 Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in E-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158–166. ACM, November 1999
4.
Zurück zum Zitat Wan, M., et al.: Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1103–1112, April 2017 Wan, M., et al.: Modeling consumer preferences and price sensitivities from large-scale grocery shopping transaction logs. In: Proceedings of the 26th International Conference on World Wide Web, pp. 1103–1112, April 2017
5.
Zurück zum Zitat Chinthalapati, V.R., Yadati, N., Karumanchi, R.: Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 36(1), 92–106 (2006)CrossRef Chinthalapati, V.R., Yadati, N., Karumanchi, R.: Learning dynamic prices in multiseller electronic retail markets with price sensitive customers, stochastic demands, and inventory replenishments. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 36(1), 92–106 (2006)CrossRef
6.
Zurück zum Zitat Musial, J., Pecero, J.E., Lopez, M.C., Fraire, H.J., Bouvry, P., Blazewicz, J.: How to efficiently solve internet shopping optimization problem with price sensitive discounts? In: 2014 11th International Conference on e-Business (ICE-B), pp. 209–215. IEEE, August 2014 Musial, J., Pecero, J.E., Lopez, M.C., Fraire, H.J., Bouvry, P., Blazewicz, J.: How to efficiently solve internet shopping optimization problem with price sensitive discounts? In: 2014 11th International Conference on e-Business (ICE-B), pp. 209–215. IEEE, August 2014
7.
Zurück zum Zitat Wei, C., Liu, Y., Zhang, M., Ma, S., Ru, L., Zhang, K.: Fighting against web spam: a novel propagation method based on click-through data. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 395–404. ACM, August 2012 Wei, C., Liu, Y., Zhang, M., Ma, S., Ru, L., Zhang, K.: Fighting against web spam: a novel propagation method based on click-through data. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 395–404. ACM, August 2012
8.
Zurück zum Zitat Aletras, N., Stevenson, M.: Labelling topics using unsupervised graph-based methods. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 631–636 (2014) Aletras, N., Stevenson, M.: Labelling topics using unsupervised graph-based methods. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 631–636 (2014)
10.
Zurück zum Zitat Muthukrishnan, P.: Unsupervised graph-based similarity learning using heterogeneous features. Doctoral dissertation, University of Michigan (2011) Muthukrishnan, P.: Unsupervised graph-based similarity learning using heterogeneous features. Doctoral dissertation, University of Michigan (2011)
11.
Zurück zum Zitat Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196, January 2014 Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196, January 2014
12.
Zurück zum Zitat Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of AUAI, pp. 452–461, June 2009 Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of AUAI, pp. 452–461, June 2009
13.
Zurück zum Zitat Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)CrossRef Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. (TOIS) 22(1), 143–177 (2004)CrossRef
14.
Zurück zum Zitat Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRef Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRef
15.
Zurück zum Zitat Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE, December 2008 Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE, December 2008
Metadaten
Titel
Identifying Price Sensitive Customers in E-commerce Platforms for Recommender Systems
verfasst von
Yingwai Shiu
Cheng Guo
Min Zhang
Yiqun Liu
Shaoping Ma
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-01012-6_18

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