poster

Modeling Retail Transaction Data for Personalized Shopping Recommendation

Authors:

Pengfei Wang,

Jiafeng Guo,

Yanyan LanAuthors Info & Claims

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Pages 1979 - 1982

https://doi.org/10.1145/2661829.2662020

Published: 03 November 2014 Publication History

Get Access

Abstract

Retail transaction data conveys rich preference information on brands and goods from customers. How to mine the transaction data to provide personalized recommendation to customers becomes a critical task for retailers. Previous recommendation methods either focus on the user-product matrix and ignore the transactions, or only use the partial information of transactions, leading to inferior performance in recommendation. Inspired by association rule mining, we introduce association pattern as a basic unit to capture the correlation between products from both intra- and intertransactions. A Probabilistic model over the Association Patterns (PAP for short) is then employed to learn the potential shopping interests and also to provide personalized recommendations. Experimental results on two real world retail data sets show that our proposed method can outperform the state-of-the-art recommendation methods.

References

[1]

G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowl. and Data Eng., 17(6):734--749, June 2005.

Digital Library

Google Scholar

[2]

K. Choi, D. Yoo, G. Kim, and Y. Suh. A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering and sequential pattern analysis. Electron. Commer. Rec. Appl., 11(4):309--317, July 2012.

Digital Library

Google Scholar

[3]

Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, Aug. 2009.

Digital Library

Google Scholar

[4]

B. Mobasher, H. Dai, T. Luo, and M. Nakagawa. Effective personalization based on association rule discovery from web usage data. In Proceedings of the 3rd International Workshop on Web Information and Data Management, WIDM '01, pages 9--15, New York, NY, USA, 2001. ACM.

Digital Library

Google Scholar

[5]

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452--461, Arlington, Virginia, United States, 2009. AUAI Press.

Digital Library

Google Scholar

[6]

S. Rendle, C. Freudenthaler, and L. Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web, WWW '10, pages 811--820, New York, NY, USA, 2010. ACM.

Digital Library

Google Scholar

[7]

X. Wu, Q. Liu, E. Chen, L. He, J. Lv, C. Cao, and G. Hu. Personalized next-song recommendation in online karaokes. In Proceedings of the 7th ACM Conference on Recommender Systems, RecSys '13, pages 137--140, New York, NY, USA, 2013. ACM.

Digital Library

Google Scholar

[8]

X. Yu, X. Ren, Y. Sun, Q. Gu, B. Sturt, U. Khandelwal, B. Norick, and J. Han. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, pages 283--292, New York, NY, USA, 2014. ACM.

Digital Library

Google Scholar

Cited By

View all

Ariannezhad M(2022)Understanding and Learning from User Behavior for Recommendation in Multi-channel RetailAdvances in Information Retrieval10.1007/978-3-030-99739-7_56(455-462)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_56
Matias LFreitas AMiranda F(2021)Retail Customer Native Baskets CreationHandbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry10.4018/978-1-7998-6985-6.ch008(168-191)Online publication date: 25-Jun-2021
https://doi.org/10.4018/978-1-7998-6985-6.ch008
Ariannezhad MJullien SNauts PFang MSchelter Sde Rijke MDemartini GZuccon GCulpepper JHuang ZTong H(2021)Understanding Multi-channel Customer Behavior in RetailProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482208(2867-2871)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482208
Show More Cited By

Index Terms

Modeling Retail Transaction Data for Personalized Shopping Recommendation
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation

A good shopping recommender system can boost sales in a retailer store. To provide accurate recommendation, the recommender needs to accurately predict a customer's preference, an ability difficult to acquire. Conventional data mining techniques, such ...
Personalized recommendation based on the personal innovator degree
RecSys '09: Proceedings of the third ACM conference on Recommender systems

This paper proposes a novel Collaborative Filtering scheme; it focuses on the dynamics and precedence of user preference to recommend items that match the latest preference of the target user. In predicting which items this user will purchase in the ...
Content-based personalised recommendation in virtual shopping environment

In our paper, we illustrate two kinds of product recommender algorithms to support e-commerce. For those commodities which a consumer seldom buys, user-rating methods are required to acquire the data set of the products rating in terms of the preference ...

Comments

Information & Contributors

Information

Published In

CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

November 2014

2152 pages

ISBN:9781450325981

DOI:10.1145/2661829

General Chairs:
Jianzhong Li
Harbin Inst. of Technology
,
X. Sean Wang
Fudan University
,
Program Chairs:
Minos Garofalakis
Technical University of Crete, Greece
,
Ian Soboroff
National Institute of Standards, USA
,
Torsten Suel
New York University, USA
,
Min Wang
Google Research, USA

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Funding Sources

The National Key Technology R&D Program under Grants
National Natural Science Foundation of China
Ministry of Science and Technology of the People's Republic of China

Conference

CIKM '14

Sponsor:

CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3 - 7, 2014

Shanghai, China

Acceptance Rates

CIKM '14 Paper Acceptance Rate 175 of 838 submissions, 21%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
554
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ariannezhad M(2022)Understanding and Learning from User Behavior for Recommendation in Multi-channel RetailAdvances in Information Retrieval10.1007/978-3-030-99739-7_56(455-462)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-030-99739-7_56
Matias LFreitas AMiranda F(2021)Retail Customer Native Baskets CreationHandbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry10.4018/978-1-7998-6985-6.ch008(168-191)Online publication date: 25-Jun-2021
https://doi.org/10.4018/978-1-7998-6985-6.ch008
Ariannezhad MJullien SNauts PFang MSchelter Sde Rijke MDemartini GZuccon GCulpepper JHuang ZTong H(2021)Understanding Multi-channel Customer Behavior in RetailProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482208(2867-2871)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482208
Wang WCao L(2021)Interactive Sequential Basket Recommendation by Learning Basket Couplings and Positive/Negative FeedbackACM Transactions on Information Systems10.1145/344436839:3(1-26)Online publication date: 23-Feb-2021
https://dl.acm.org/doi/10.1145/3444368
Sharma AOmair Shafiq M(2020)Predicting purchase probability of retail items using an ensemble learning approach and historical data2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA51294.2020.00118(723-728)Online publication date: Dec-2020
https://doi.org/10.1109/ICMLA51294.2020.00118
Guidotti RViotto S(2020)Interpretable Next Basket Prediction Boosted with Representative Recipes2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)10.1109/CogMI50398.2020.00018(62-71)Online publication date: Oct-2020
https://doi.org/10.1109/CogMI50398.2020.00018
Sagar DGarg JKansal PBhalla SShah RYu Y(2020)PAI-BPR: Personalized Outfit Recommendation Scheme with Attribute-wise Interpretability2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM)10.1109/BigMM50055.2020.00039(221-230)Online publication date: Sep-2020
https://doi.org/10.1109/BigMM50055.2020.00039
Li MBao XChang LXu ZLi L(2020)A Survey of Researches on Personalized Bundle Recommendation TechniquesMachine Learning for Cyber Security10.1007/978-3-030-62460-6_26(290-304)Online publication date: 8-Oct-2020
https://dl.acm.org/doi/10.1007/978-3-030-62460-6_26
Bhatt SPadhee SSheth AChen KShalin VDoran DMinnery BCulpepper JMoffat ABennett PLerman K(2019)Knowledge Graph Enhanced Community Detection and CharacterizationProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291031(51-59)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3291031
Guidotti RRossetti GPappalardo LGiannotti FPedreschi D(2019)Personalized Market Basket Prediction with Temporal Annotated Recurring SequencesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.287258731:11(2151-2163)Online publication date: 1-Nov-2019
https://doi.org/10.1109/TKDE.2018.2872587
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Mining Skewed and Sparse Transaction Data for Personalized Shopping Recommendation

Personalized recommendation based on the personal innovator degree

Content-based personalised recommendation in virtual shopping environment

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Funding Sources

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations