research-article

Session-based item recommendation with pairwise features

Authors:

Zhe Wang,

Yangbo Gao,

Huan Chen,

Peng YanAuthors Info & Claims

RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge

Article No.: 3, Pages 1 - 5

https://doi.org/10.1145/3359555.3359559

Published: 20 September 2019 Publication History

Get Access

Abstract

The RecSys Challenge 2019 seeks a better solution for item recommendation on short session-based data with limited user history. This paper describes the team PVZ's approach to this challenge, which won the 3rd place in the contest. Our solution consists of the following components. Firstly, we cast the hotel recommendation task as a binary classification problem. Secondly, we spend most of the time doing feature engineering and mining a series of useful features in various aspects. Then we train individual models with a different set of features and blend them with some important features using stacking method. At last, we create other new pair-wise features based on the existing model predictions and train a stacking model again which generates our final result. Our final solution achieved a public score of 0.685929 and a private score of 0.684071, ranking the third place on both sides.

References

[1]

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993--1022.

Digital Library

Google Scholar

[2]

Christopher J Burges, Robert Ragno, and Quoc V Le. 2007. Learning to rank with nonsmooth cost functions. In Advances in neural information processing systems. 193--200.

Google Scholar

[3]

Tianqi Chen and Carlos Guestrin. 2016. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 785--794.

Digital Library

Google Scholar

[4]

Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.

Google Scholar

[5]

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems. 3146--3154.

Digital Library

Google Scholar

[6]

Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard-Paul Leyson, and Philipp Monreal. 2019. RecSys Challenge 2019: Session-based Hotel Recommendations. In Proceedings of the Thirteenth ACM Conference on Recommender Systems (RecSys '19). ACM, New York, NY, USA, 2.

Digital Library

Google Scholar

[7]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).

Google Scholar

[8]

Steffen Rendle. 2010. Factorization machines. In 2010 IEEE International Conference on Data Mining. IEEE, 995--1000.

Digital Library

Google Scholar

[9]

Qiang Wu, Christopher JC Burges, Krysta M Svore, and Jianfeng Gao. 2010. Adapting boosting for information retrieval measures. Information Retrieval 13, 3 (2010), 254--270.

Digital Library

Google Scholar

[10]

Peng Yan, Xiaocong Zhou, and Yitao Duan. 2015. E-commerce item recommendation based on field-aware factorization machine. In Proceedings of the 2015 International ACM Recommender Systems Challenge. ACM, 2.

Digital Library

Google Scholar

[11]

Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, and Kun Gai. 2019. Deep interest evolution network for click-through rate prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5941--5948.

Digital Library

Google Scholar

Cited By

View all

Mao YLin SCheng Y(2024)A Job Recommendation Model Based on a Two-Layer Attention MechanismElectronics10.3390/electronics1303048513:3(485)Online publication date: 24-Jan-2024
https://doi.org/10.3390/electronics13030485
Bennawy Mel-Kafrawy P(2022)Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender SystemIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_59(514-523)Online publication date: 2-Jul-2022
https://doi.org/10.1007/978-3-031-09176-6_59
Jannach Dde Souza P. Moreira GOldridge E(2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3415959.3416001
Show More Cited By

Index Terms

Session-based item recommendation with pairwise features
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Item recommendation in collaborative tagging systems via heuristic data fusion

Collaborative tagging systems have been popular on the Web. However, information overload results in the increasing need for recommender services from users, and thus item recommendation has been one of the key issues in such systems. In this paper, we ...
Item recommendation based on context-aware model for personalized u-healthcare service

A personalized service in the ubiquitous environment is to provide services or items, which reflect personal tastes, attitudes, and contexts. It is impossible to reflect the context information generated in u-healthcare environments due to the existing ...
Conditional restricted Boltzmann machine for item recommendation
Abstract
Recommender systems provide an excellent solution to the issue of information overload by generating item recommendation from a huge collection of items based on users’ preferences. In terms of modeling users’ rating data, existing ...

Comments

Information & Contributors

Information

Published In

RecSys Challenge '19: Proceedings of the Workshop on ACM Recommender Systems Challenge

September 2019

49 pages

ISBN:9781450376679

DOI:10.1145/3359555

Program Chairs:
Peter Knees
TU Wien, Austria
,
Yashar Deldjoo
Polytechnic University of Bari, Italy
,
Farshad Bakhshandegan Moghaddam
Karlsruhe Institute ofTechnology, Germany
,
Jens Adamczak
trivago N.V., Germany
,
Gerard Leyson
trivago N.V., Germany
,
Philipp Monreal
trivago N.V., Germany

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: 20 September 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

RecSys Challenge '19

RecSys Challenge '19: ACM Recommender Systems Challenge 2019 Workshop

September 20, 2019

Copenhagen, Denmark

Acceptance Rates

Overall Acceptance Rate 11 of 15 submissions, 73%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
214
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Mao YLin SCheng Y(2024)A Job Recommendation Model Based on a Two-Layer Attention MechanismElectronics10.3390/electronics1303048513:3(485)Online publication date: 24-Jan-2024
https://doi.org/10.3390/electronics13030485
Bennawy Mel-Kafrawy P(2022)Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender SystemIntelligent and Fuzzy Systems10.1007/978-3-031-09176-6_59(514-523)Online publication date: 2-Jul-2022
https://doi.org/10.1007/978-3-031-09176-6_59
Jannach Dde Souza P. Moreira GOldridge E(2020)Why Are Deep Learning Models Not Consistently Winning Recommender Systems Competitions Yet?Proceedings of the Recommender Systems Challenge 202010.1145/3415959.3416001(44-49)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3415959.3416001
Adamczak JDeldjoo YMoghaddam FKnees PLeyson GMonreal P(2020)Session-based Hotel Recommendations DatasetACM Transactions on Intelligent Systems and Technology10.1145/341237912:1(1-20)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3412379

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

Item recommendation in collaborative tagging systems via heuristic data fusion

Item recommendation based on context-aware model for personalized u-healthcare service

Conditional restricted Boltzmann machine for item recommendation

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

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