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E-Commerce Item Recommendation Based on Field-aware Factorization Machine

Authors:

Peng Yan,

Xiaocong Zhou,

Yitao DuanAuthors Info & Claims

RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge

Article No.: 2, Pages 1 - 4

https://doi.org/10.1145/2813448.2813511

Published: 16 September 2015 Publication History

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Abstract

The RecSys 2015 contest [1] seeks the best solution to a top-N e-commerce item recommendation problem. This paper describes the team Random Walker's approach to this challenge, which won the 3rd place in the contest. Our solution consists of the following components. Firstly, we cast the top-N recommendation task into a binary classification problem and extract original features from the raw data. Secondly, we learn derived features using field-aware factorization machines (FFM) and gradient boosting decision tree (GBDT). Lastly, we train 2 FFM models with different feature sets and combine them by a non-linear weighted blending. This solution is the result of numerous tests and the scheme turns out to be effective. Our final solution achieved a score of 61075.2, ranking in the third place on the public leaderboard.

References

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Y.-C. Juan. Libffm: A library for field-aware factorization machines. http://www.csie.ntu.edu.tw/cjlin/libffm/, 2015.

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Cited By

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Han HLiang YBella GGiunchiglia FLi D(2023)LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBMEntropy10.3390/e2504063825:4(638)Online publication date: 10-Apr-2023
https://doi.org/10.3390/e25040638
Wang XYang PChen SLiu LZhao LGuo JSun MLi PDemartini GZuccon GCulpepper JHuang ZTong H(2021)Efficient Learning to Learn a Robust CTR Model for Web-scale Online Sponsored Search AdvertisingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481912(4203-4213)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3481912
Liu YLi CXiao HCai J(2021)GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network InteractionIEEE Access10.1109/ACCESS.2021.31167059(140022-140030)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3116705
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Index Terms

E-Commerce Item Recommendation Based on Field-aware Factorization Machine
1. Information systems
  1. Information systems applications

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RecSys '15 Challenge: Proceedings of the 2015 International ACM Recommender Systems Challenge

September 2015

53 pages

ISBN:9781450336659

DOI:10.1145/2813448

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: 16 September 2015

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RecSys '15

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SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

Acceptance Rates

RecSys '15 Challenge Paper Acceptance Rate 12 of 21 submissions, 57%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Han HLiang YBella GGiunchiglia FLi D(2023)LFDNN: A Novel Hybrid Recommendation Model Based on DeepFM and LightGBMEntropy10.3390/e2504063825:4(638)Online publication date: 10-Apr-2023
https://doi.org/10.3390/e25040638
Wang XYang PChen SLiu LZhao LGuo JSun MLi PDemartini GZuccon GCulpepper JHuang ZTong H(2021)Efficient Learning to Learn a Robust CTR Model for Web-scale Online Sponsored Search AdvertisingProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481912(4203-4213)Online publication date: 26-Oct-2021
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Liu YLi CXiao HCai J(2021)GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network InteractionIEEE Access10.1109/ACCESS.2021.31167059(140022-140030)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3116705
Lang FLiang LHuang KChen TZhu S(2021)Movie Recommendation System for Educational Purposes Based on Field-Aware Factorization MachineMobile Networks and Applications10.1007/s11036-021-01775-9Online publication date: 13-May-2021
https://doi.org/10.1007/s11036-021-01775-9
Wen YWang FWu RLiu JCao B(2020)Improving the novelty of retail commodity recommendations using multiarmed bandit and gradient boosting decision treeConcurrency and Computation: Practice and Experience10.1002/cpe.570332:14Online publication date: 27-Feb-2020
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Bai GLien CChen H(2019)Co-learning Multiple Browsing Tendencies of a User by Matrix Factorization-based Multitask LearningIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352526(253-257)Online publication date: 14-Oct-2019
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HOU CCHEN CWANG J(2018)Tree-Based Feature Transformation for Purchase Behavior PredictionIEICE Transactions on Information and Systems10.1587/transinf.2017EDL8210E101.D:5(1441-1444)Online publication date: 1-May-2018
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N CRavindran BRanu SGanguly NRamakrishnan RSarawagi SRoy S(2018)A neural attention based approach for clickstream miningProceedings of the ACM India Joint International Conference on Data Science and Management of Data10.1145/3152494.3152505(118-127)Online publication date: 11-Jan-2018
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Bai GZhan Z(2018)Application of Deep Autoencoders in Commerce RecommendationAdvances in Computer Communication and Computational Sciences10.1007/978-981-13-0341-8_22(235-242)Online publication date: 23-Aug-2018
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Recommendations

Local Item-Item Models For Top-N Recommendation

Personalized hybrid recommendation for group of users

Block-Aware Item Similarity Models for Top-N Recommendation

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