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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Interaction-Aware Factorization Machines for Recommender Systems

February 1, 2023

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Authors

Fuxing Hong

Tencent


Dongbo Huang

Tencent


Ge Chen

Tencent


DOI:

10.1609/aaai.v33i01.33013804


Abstract:

Factorization Machine (FM) is a widely used supervised learning approach by effectively modeling of feature interactions. Despite the successful application of FM and its many deep learning variants, treating every feature interaction fairly may degrade the performance. For example, the interactions of a useless feature may introduce noises; the importance of a feature may also differ when interacting with different features. In this work, we propose a novel model named Interaction-aware Factorization Machine (IFM) by introducing Interaction-Aware Mechanism (IAM), which comprises the feature aspect and the field aspect, to learn flexible interactions on two levels. The feature aspect learns feature interaction importance via an attention network while the field aspect learns the feature interaction effect as a parametric similarity of the feature interaction vector and the corresponding field interaction prototype. IFM introduces more structured control and learns feature interaction importance in a stratified manner, which allows for more leverage in tweaking the interactions on both feature-wise and field-wise levels. Besides, we give a more generalized architecture and propose Interaction-aware Neural Network (INN) and DeepIFM to capture higher-order interactions. To further improve both the performance and efficiency of IFM, a sampling scheme is developed to select interactions based on the field aspect importance. The experimental results from two well-known datasets show the superiority of the proposed models over the state-of-the-art methods.

Topics: AAAI

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HOW TO CITE:

Fuxing Hong||Dongbo Huang||Ge Chen Interaction-Aware Factorization Machines for Recommender Systems Proceedings of the AAAI Conference on Artificial Intelligence (2019) 3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen Interaction-Aware Factorization Machines for Recommender Systems AAAI 2019, 3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen (2019). Interaction-Aware Factorization Machines for Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen. Interaction-Aware Factorization Machines for Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen. 2019. Interaction-Aware Factorization Machines for Recommender Systems. "Proceedings of the AAAI Conference on Artificial Intelligence". 3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen. (2019) "Interaction-Aware Factorization Machines for Recommender Systems", Proceedings of the AAAI Conference on Artificial Intelligence, p.3804-3811

Fuxing Hong||Dongbo Huang||Ge Chen, "Interaction-Aware Factorization Machines for Recommender Systems", AAAI, p.3804-3811, 2019.

Fuxing Hong||Dongbo Huang||Ge Chen. "Interaction-Aware Factorization Machines for Recommender Systems". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen. "Interaction-Aware Factorization Machines for Recommender Systems". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 3804-3811.

Fuxing Hong||Dongbo Huang||Ge Chen. Interaction-Aware Factorization Machines for Recommender Systems. AAAI[Internet]. 2019[cited 2023]; 3804-3811.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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