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Utilizing Purchase Intervals in Latent Clusters for Product Recommendation

Authors:

Hsu WynneAuthors Info & Claims

SNAKDD'14: Proceedings of the 8th Workshop on Social Network Mining and Analysis

Article No.: 4, Pages 1 - 9

https://doi.org/10.1145/2659480.2659496

Published: 24 August 2014 Publication History

Abstract

Collaborative filtering have become increasingly important with the development of Web 2.0. Online shopping service providers aim to provide users with quality list of recommended items that will enhance user satisfaction and loyalty. Matrix factorization approaches have become the dominant method as they can reduce the dimension of the data set and alleviate the sparsity problem. However, matrix factorization approaches are limited because they depict each user as one preference vector. In practice, we observe that users may have different preferences when purchasing different subsets of items, and the periods between purchases also vary from one user to another. In this work, we propose a probabilistic approach to learn latent clusters in the large user-item matrix, and incorporate temporal information into the recommendation process. Experimental results on a real world dataset demonstrate that our approach significantly improves the conversion rate, precision and recall of state-of-the-art methods.

References

[1]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, March 2003.

[2]

C. Cobb and P. Douglas. Theory of production. American Economic Review, 18:139--465, 1928.

[3]

Th. George and S. Merugu. A scalable collaborative filtering framework based on co-clustering. In Fifth IEEE International Conference on Data Mining, pages 4--pp. IEEE, 2005.

Digital Library

[4]

M.D. Hoffman, D.M. Blei, and F. Bach. Online learning for latent dirichlet allocation. Advances in Neural Information Processing Systems, 23:856--864, 2010.

Digital Library

[5]

T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference, pages 50--57, 1999.

Digital Library

[6]

T. Hofmann. Latent semantic models for collaborative filtering. ACM TOIS, 22:89--115, 2004.

Digital Library

[7]

Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 263--272, 2008.

Digital Library

[8]

T. G. Kolda and J. Sun. Scalable tensor decompositions for multi-aspect data mining. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 363--372, Washington, DC, USA, 2008. IEEE Computer Society.

Digital Library

[9]

Y. Koren. Collaborative filtering with temporal dynamics. pages 89--97, 2009.

[10]

Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Journal of IEEE COMPUTER, 42(8):30--37, August 2009.

Digital Library

[11]

B. Li, A. Ghose, and P.G. Ipeirotis. Towards a theory model for product search. In WWW Conference, pages 327--336, 2011.

Digital Library

[12]

L. Lü, M. Medo, C. H. Yeung, Y. Zhang, Z. Zhang, and T. Zhou. Recommender systems. Physics Reports, 519(1):1--49, 2012.

[13]

S. C. Madeira and A. L. Oliveira. Biclustering algorithms for biological data analysis: a survey. Computational Biology and Bioinformatics, IEEE/ACM Transactions on, 1(1):24--45, 2004.

Digital Library

[14]

S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In WWW Conference, pages 811--820, 2010.

Digital Library

[15]

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285--295, 2001.

Digital Library

[16]

B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering. In Proceedings of the fifth international conference on computer and information technology, volume 1, 2002.

[17]

P. Symeonidis, A.s Nanopoulos, A. Papadopoulos, and Y. Manolopoulos. Nearest-biclusters collaborative filtering. 2006.

[18]

L. H. Ungar and D. P. Foster. Clustering methods for collaborative filtering. In AAAI Workshop on Recommendation Systems, number 1, 1998.

[19]

S. Vucetic and Z. Obradovic. Collaborative filtering using a regression-based approach. Knowl. Inf. Syst., 7(1):1--22, January 2005.

Digital Library

[20]

J. Wang, B. Sarwar, and N. Sundaresan. Utilizing related products for post-purchase recommendation in e-commerce. In Proceedings of the fifth ACM conference on Recommender systems, pages 329--332. ACM, 2011.

Digital Library

[21]

J. Wang and Y. Zhang. Utilizing marginal net utility for recommendation in e-commerce. In Proceedings of the 35th international ACM SIGIR conference, pages 1003--1012, 2011.

Digital Library

[22]

J. Wang and Y. Zhang. Opportunity model for e-commerce recommendation: right product; right time. In Proceedings of the 35th international ACM SIGIR conference, pages 303--312. ACM, 2013.

Digital Library

[23]

L. Xiang, Q. Yuan, and S. et. al. Zhao. Temporal recommendation on graphs via long- and short-term preference fusion. In ACM SIGKDD, pages 723--732, 2010.

Digital Library

[24]

B. Xu, J. Bu, C. Chen, and D. Cai. An exploration of improving collaborative recommender systems via user-item subgroups. In Proceedings of the 21st international conference on World Wide Web, pages 21--30, New York, NY, USA, 2012.

Digital Library

[25]

G. Zeno, R. Steffen, F. Christoph, and S. Lars. MyMediaLite: A free recommender system library. In Proceedings of the 5th ACM Conference on Recommender Systems (RecSys 2011), 2011.

Digital Library

[26]

G. Zhao, M. L. Lee, W. Hsu, and W. Chen. Increasing temporal diversity with purchase intervals. In Proceedings of the 35th international ACM SIGIR conference, pages 165--174, 2012.

Digital Library

[27]

G. Zhao, M. L. Lee, W. Hsu, W. Chen, and H. Hu. Community-based user recommendation in uni-directional social networks. In Proceedings of the 21st international ACM CIKM conference, pages 189--198, 2013.

Digital Library

Cited By

Liu BZhang HKong LNiu D(2022)Factorizing Historical User Actions for Next-Day Purchase PredictionACM Transactions on the Web10.1145/346822716:1(1-26)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3468227
Kim SCho H(2020)User–Topic Modeling for Online Community AnalysisApplied Sciences10.3390/app1010338810:10(3388)Online publication date: 14-May-2020
https://doi.org/10.3390/app10103388
Quadrana MCremonesi PJannach D(2018)Sequence-Aware Recommender SystemsACM Computing Surveys10.1145/319061651:4(1-36)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3190616
Show More Cited By

Index Terms

Utilizing Purchase Intervals in Latent Clusters for Product Recommendation
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

SNAKDD'14: Proceedings of the 8th Workshop on Social Network Mining and Analysis

August 2014

90 pages

ISBN:9781450331920

DOI:10.1145/2659480

Program Chair:
Feida Zhu

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

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Cited By

Liu BZhang HKong LNiu D(2022)Factorizing Historical User Actions for Next-Day Purchase PredictionACM Transactions on the Web10.1145/346822716:1(1-26)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3468227
Kim SCho H(2020)User–Topic Modeling for Online Community AnalysisApplied Sciences10.3390/app1010338810:10(3388)Online publication date: 14-May-2020
https://doi.org/10.3390/app10103388
Quadrana MCremonesi PJannach D(2018)Sequence-Aware Recommender SystemsACM Computing Surveys10.1145/319061651:4(1-36)Online publication date: 6-Jul-2018
https://dl.acm.org/doi/10.1145/3190616
Yang JLiu CTeng MChen JXiong H(2018)A Unified View of Social and Temporal Modeling for B2B Marketing Campaign RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.278392630:5(810-823)Online publication date: 1-May-2018
https://doi.org/10.1109/TKDE.2017.2783926
Boratto LCarta SFenu GMulas FPilloni P(2018)Influence of Rating Prediction on Group Recommendation's AccuracyIEEE Intelligent Systems10.1109/MIS.2016.10031:6(22-27)Online publication date: 25-Dec-2018
https://dl.acm.org/doi/10.1109/MIS.2016.100
Guo JGao ZLiu NWu Y(2018)Recommend products with consideration of multi-category inter-purchase time and priceFuture Generation Computer Systems10.1016/j.future.2017.02.03178:P1(451-461)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1016/j.future.2017.02.031
Dey SMitra PGupta KSen SGeyer WFreyne JCastells P(2016)Recommending Repeat Purchases using Product Segment StatisticsProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959145(357-360)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959145

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