Abstract
Basket recommendation is an important task in market basket analysis. Existing work on this problem can be summarized into two paradigms. One is the item-centric paradigm, where sequential patterns are mined from users’ transactional data and leveraged for prediction. However, these approaches usually suffer from the data sparseness problem. The other is the user-centric paradigm, where collaborative filtering techniques have been applied on users’ historical data. However, these methods ignore the sequential behaviors of users, which are often crucial for basket recommendation. In this paper, we introduce a hybrid method, namely the Co-Factorization model over Sequential and Historical purchase data (CFSH for short) for basket recommendation. Compared with existing methods, our approach enjoys the following merits: (1) By mining and factorizing global sequential patterns, we can avoid the sparseness problem in traditional item-centric methods; (2) By factorizing item-item and user-item matrices simultaneously, we can exploit both sequential and historical behaviors to learn user and item representations better; (3) Experimental results on three real-world transaction datasets demonstrated the effectiveness of our approach as compared with the existing methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Reutterer, T., Mild, A.: An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. J. Retail. Consum. Serv. 10(3), 123–133 (2003)
Meek, C., Zimdars, A., Chickering, D.M.: Using temporal data for making recommendations. UAI (2001)
Cavique, L.: A scalable algorithm for the market basket analysis. J. Retail. Consum. Serv. 14, 400–407 (2007)
Chen, J., Shankar, S., Kelly, A., Gningue, S., Rajaravivarma, R.: A two stage approach for contiguous sequential pattern mining. In: Proceedings of the 10th IEEE International Conference on Information Reuse & Integration (2009)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: SIGKDD (2006)
Gatzioura, A., Sanchez Marre, M.: A case-based recommendation approach for market basket data. IEEE Intell. Syst. 30, 20–27 (2014)
Godoy, D., Amandi, A.: User profiling in personal information agents: a survey. Knowl. Eng. Rev. 20(4), 329–361 (2005)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272. IEEE Computer Society, Washington, DC (2008)
Kim, S., Lee, J.-S., Jun, C.-H., Lee, J.: Classification-based collaborative filtering using market basket data. Expert Syst. Appl. 29, 700–704 (2005)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM (2001)
Koenigstein, N., Koren, Y.: Towards scalable and accurate item-oriented recommendations. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 419–422. ACM, New York (2013)
Lacerda, A., Ziviani, N.: Building user profiles to improve user experience in recommender systems. In: WSDM, WSDM 2013, pp. 759–764. ACM, New York (2013)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Leen, T., Dietterich, T., Tresp, V. (eds.) Advances in Neural Information Processing Systems 13, pp. 556–562. MIT Press, Cambridge (2001)
Lin, W., Alvarez, S.A., Ruiz, C.: Efficient adaptive-support association rule mining for recommender systems. Data Min. Knowl. Discov. 6(1), 83–105 (2002)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Mobasher, B.: Using sequential and non-sequential patterns in predictive web usage mining tasks. In: The IEEE International Conference on Data Mining Series (2002)
Forsatia, C.R., Meybodib, M.R.: Effective page recommendation algorithms based on distributed learning automata and weighted association rules. Expert Syst. Appl. 37, 1316–1330 (2010)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: UAI (2009)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4:2 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Wang, P., Guo, J., Lan, Y., Xu, J., Cheng, X. (2016). Factorizing Sequential and Historical Purchase Data for Basket Recommendation. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-48051-0_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-48050-3
Online ISBN: 978-3-319-48051-0
eBook Packages: Computer ScienceComputer Science (R0)