TY - GEN
T1 - AD-AUG
T2 - 22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
AU - Wang, Yifan
AU - Qin, Yifang
AU - Han, Yu
AU - Yin, Mingyang
AU - Zhou, Jingren
AU - Yang, Hongxia
AU - Zhang, Ming
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/9
Y1 - 2022/9
N2 - Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply in practice due to the sparsity and bias of the real-world user feedback data. In this paper, we propose a novel counterfactual data augmentation framework AD-AUG to mitigate the impact of the imperfect training data and empower CF models. The key idea of AD-AUG is to answer the counterfactual question: “what would be a user’s feedback if his previous purchase history had been different?”. Our framework is composed of an augmenter model and a recommender model. The augmenter model aims to generate counterfactual user feedback based on the observed ones, while the recommender leverages the original and counterfactual user feedback data to provide the final recommendation. In particular, we design two adversarial learning-based methods from both “bottom-up” data-oriented and “top-down” model-oriented perspectives for counterfactual learning. Extensive experiments on three real-world datasets show that the AD-AUG can greatly enhance a wide range of CF models, demonstrating our framework’s effectiveness and generality.
AB - Collaborative filtering (CF) has become one of the most popular and widely used methods in recommender systems, but its performance degrades sharply in practice due to the sparsity and bias of the real-world user feedback data. In this paper, we propose a novel counterfactual data augmentation framework AD-AUG to mitigate the impact of the imperfect training data and empower CF models. The key idea of AD-AUG is to answer the counterfactual question: “what would be a user’s feedback if his previous purchase history had been different?”. Our framework is composed of an augmenter model and a recommender model. The augmenter model aims to generate counterfactual user feedback based on the observed ones, while the recommender leverages the original and counterfactual user feedback data to provide the final recommendation. In particular, we design two adversarial learning-based methods from both “bottom-up” data-oriented and “top-down” model-oriented perspectives for counterfactual learning. Extensive experiments on three real-world datasets show that the AD-AUG can greatly enhance a wide range of CF models, demonstrating our framework’s effectiveness and generality.
KW - Collaborative filtering
KW - Counterfactual augmentation
KW - Recommending systems
UR - https://www.scopus.com/pages/publications/85151046124
U2 - 10.1007/978-3-031-26387-3_29
DO - 10.1007/978-3-031-26387-3_29
M3 - Conference article published in proceeding or book
AN - SCOPUS:85151046124
SN - 9783031263866
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 474
EP - 490
BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
A2 - Amini, Massih-Reza
A2 - Canu, Stéphane
A2 - Fischer, Asja
A2 - Guns, Tias
A2 - Kralj Novak, Petra
A2 - Tsoumakas, Grigorios
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 September 2022 through 23 September 2022
ER -