Bayesian feature interaction selection for factorization machines

Chen, Yifan; Wang, Yang; Ren, Pengjie; Wang, Meng; Rijke, Maarten de

doi:10.1016/j.artint.2021.103589

Cited by 17 publications

(8 citation statements)

References 66 publications

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“…As a hot issue widely concerned, cross-modal retrieval problem is studied by a growing number of researchers [4,5,25,29,35,40,50]. According to the representation type of multimedia instances, cross-modal retrieval can be divided into two groups: real-valued representation based retrieval and binary representation (hash code) based retrieval.…”

Section: Related Workmentioning

confidence: 99%

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

Zhu

Cai

Song

et al. 2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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Cross-modal hashing is a hot issue in the multimedia community, which is to generate compact hash code from multimedia content for efficient cross-modal search. Two challenges, i.e., (1) How to efficiently enhance cross-modal semantic mining is essential for cross-modal hash code learning, and (2) How to combine multiple semantic correlations learning to improve the semantic similarity preserving, cannot be ignored. To this end, this paper proposed a novel end-to-end cross-modal hashing approach, named Multiple Semantic Structure-Preserving Quantization (MSSPQ) that is to integrate deep hashing model with multiple semantic correlation learning to boost hash learning performance. The multiple semantic correlation learning consists of inter-modal and intra-modal pairwise correlation learning and Cosine correlation learning, which can comprehensively capture cross-modal consistent semantics and realize semantic similarity preserving. Extensive experiments are conducted on three multimedia datasets, which confirms that the proposed method outperforms the baselines. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval.

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Section: Related Workmentioning

confidence: 99%

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

Zhu

Cai

Song

et al. 2022

Proceedings of the 2022 International Conference on Multimedia Retrieval

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“…Chen et al. [ 45 ] propose a Bayesian higher-order feature interaction selection (BH-FIS) to perform the FIS. First, the outer product and masking techniques are utilized to enumerate all feature interactions.…”

Section: Related Workmentioning

confidence: 99%

GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction

Liu¹,

Ma²,

Wang³

2022

Sensors

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CTR (Click-Through Rate) prediction has attracted more and more attention from academia and industry for its significant contribution to revenue. In the last decade, learning feature interactions have become a mainstream research direction, and dozens of feature interaction-based models have been proposed for the CTR prediction task. The most common approach for existing models is to enumerate all possible feature interactions or to learn higher-order feature interactions by designing complex models. However, a simple enumeration will introduce meaningless and harmful interactions, and a complex model structure will bring a higher complexity. In this work, we propose a lightweight, yet effective model called the Gated Adaptive feature Interaction Network (GAIN). We devise a novel cross module to drop meaningless feature interactions and preserve informative ones. Our cross module consists of multiple gated units, each of which can independently learn an arbitrary-order feature interaction. We combine the cross module with a deep module into GAIN and conduct comparative experiments with state-of-the-art models on two public datasets to verify its validity. Our experimental results show that GAIN can achieve a comparable or even better performance compared to its competitors. Furthermore, in order to verify the effectiveness of the feature interactions learned by GAIN, we transfer learned interactions to other models, such as Logistic Regression (LR) and Factorization Machines (FM), and find out that their performance can be significantly improved.

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“…They learn and find specific patterns in the data for users' preference by probability and machine learning. Model-based methods mainly include clustering algorithms [11], classification algorithms [12], Bayesian algorithms [13], neural network algorithms [14], [15], and the latent factor model algorithms [16], [17].…”

Section: Introductionmentioning

confidence: 99%

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

Yan

Zhang

2023

Chinese J. Elect.

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To solve the rating prediction problems of low accuracy and data sparsity on different datasets, we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF, by fusing dual convolutional autoencoder (DCAE) and probability matrix factorization (PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error (RMSE) and mean absolute error (MAE).

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Bayesian feature interaction selection for factorization machines

Cited by 17 publications

References 66 publications

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

MSSPQ: Multiple Semantic Structure-Preserving Quantization for Cross-Modal Retrieval

GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

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