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Spectrum-enhanced Pairwise Learning to Rank

Authors:

Zheng QinAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 2247 - 2257

https://doi.org/10.1145/3308558.3313478

Published: 13 May 2019 Publication History

Abstract

To enhance the performance of the recommender system, side information is extensively explored with various features (e.g., visual features and textual features). However, there are some demerits of side information: (1) the extra data is not always available in all recommendation tasks; (2) it is only for items, there is seldom high-level feature describing users. To address these gaps, we introduce the spectral features extracted from two hypergraph structures of the purchase records. Spectral features describe the similarity of users/items in the graph space, which is critical for recommendation. We leverage spectral features to model the users' preference and items' properties by incorporating them into a Matrix Factorization (MF) model.

In addition to modeling, we also use spectral features to optimize. Bayesian Personalized Ranking (BPR) is extensively leveraged to optimize models in implicit feedback data. However, in BPR, all missing values are regarded as negative samples equally while many of them are indeed unseen positive ones. We enrich the positive samples by calculating the similarity among users/items by the spectral features. The key ideas are: (1) similar users shall have similar preference on the same item; (2) a user shall have similar perception on similar items. Extensive experiments on two real-world datasets demonstrate the usefulness of the spectral features and the effectiveness of our spectrum-enhanced pairwise optimization. Our models outperform several state-of-the-art models significantly.

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Cited By

Guo XWang LZhang MChen G(2023)First Things First? Order Effects in Online Product Recommender SystemsACM Transactions on Computer-Human Interaction10.1145/355788630:1(1-35)Online publication date: 18-Mar-2023
https://dl.acm.org/doi/10.1145/3557886
Wang XFukumoto FCui JSuzuki YLi JYu DChen HDuh WHuang HKato MMothe JPoblete B(2023)EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591678(383-392)Online publication date: 18-Jul-2023
https://doi.org/10.1145/3539618.3591678
Liu BWang B(2023)Pairwise learning for personalized ranking with noisy comparisonsInformation Sciences10.1016/j.ins.2022.12.028623(242-257)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.12.028
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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Cited By

Guo XWang LZhang MChen G(2023)First Things First? Order Effects in Online Product Recommender SystemsACM Transactions on Computer-Human Interaction10.1145/355788630:1(1-35)Online publication date: 18-Mar-2023
https://dl.acm.org/doi/10.1145/3557886
Wang XFukumoto FCui JSuzuki YLi JYu DChen HDuh WHuang HKato MMothe JPoblete B(2023)EEDN: Enhanced Encoder-Decoder Network with Local and Global Context Learning for POI RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591678(383-392)Online publication date: 18-Jul-2023
https://doi.org/10.1145/3539618.3591678
Liu BWang B(2023)Pairwise learning for personalized ranking with noisy comparisonsInformation Sciences10.1016/j.ins.2022.12.028623(242-257)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.12.028
Keyhanipour A(2023)Graph-based comparative analysis of learning to rank datasetsInternational Journal of Data Science and Analytics10.1007/s41060-023-00406-8Online publication date: 30-Jun-2023
https://doi.org/10.1007/s41060-023-00406-8
Yu WLin XLiu JGe JOu WQin Z(2022)Self-Propagation Graph Neural Network for RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.307677234:12(5993-6002)Online publication date: 1-Dec-2022
https://doi.org/10.1109/TKDE.2021.3076772
Wang CYang YSuo KWang P(2022)MulSetRank: Multiple set ranking for personalized recommendation from implicit feedbackKnowledge-Based Systems10.1016/j.knosys.2022.108946249(108946)Online publication date: Aug-2022
https://doi.org/10.1016/j.knosys.2022.108946
Shi PYu WLiu YQin Z(2021)Dual Convolutional Neural Network for Lung Nodule Classification2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9533336(1-7)Online publication date: 18-Jul-2021
https://doi.org/10.1109/IJCNN52387.2021.9533336
Li HGao MZhou FWang YFan QYang L(2021)Fusing hypergraph spectral features for shilling attack detectionJournal of Information Security and Applications10.1016/j.jisa.2021.10305163(103051)Online publication date: Dec-2021
https://doi.org/10.1016/j.jisa.2021.103051
Yu WQin ZHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Sampler Design for Implicit Feedback Data by Noisy-label Robust LearningProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401155(861-870)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401155

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