Titlebook: Database Systems for Advanced Applications; 26th International C Christian S. Jensen,Ee-Peng Lim,Chih-Ya Shen Conference proceedings 2021 T

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Learning Disentangled User Representation Based on Controllable VAE for Recommendationepresentation of users can uncover user intentions behind the observed data (i.e. user-item interaction) and improve the robustness and interpretability of the recommender system. However, existing collaborative filtering methods learning disentangled representation face problems of balancing the tr

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DFCN: An Effective Feature Interactions Learning Model for Recommender Systemsance of recommendation, which is of great significance. Manual feature engineering is no longer applicable due to its high cost and low efficiency. Factorization machines introduce the second-order feature interactions to enhance learning ability. Deep neural networks (DNNs) have good nonlinear comb

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MISS: A Multi-user Identification Network for Shared-Account Session-Aware Recommendationct the next interaction based on user’s historical sessions and current session. Though existing methods have achieved promising results, they still have drawbacks in some aspects. First, most existing deep learning methods model a session as a sequence, but neglect the complex transition relationsh

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Deep User Representation Construction Model for Collaborative Filteringas modeling the user-item interaction and the only difference between them is that they adopt different ways to build user representations. User-item methods obtain user representations by directly assigning each user a real-valued vector and do not consider users’ historical item information. Howev

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DiCGAN: A Dilated Convolutional Generative Adversarial Network for Recommender Systems users’ preferences. However, most existing GAN-based recommendation methods only exploit the user-item interactions, while ignoring to leverage the information between user’s interacted items. On the other hand, Convolutional Neural Network (CNN) has shown its power in learning high-order correlati

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Mau Mau Inventions and Reinventionspapers leverage abundant data from heterogeneous information sources to grasp diverse preferences and improve overall accuracy. Some noticeable papers proposed to extract users’ preference from information along with ratings such as reviews or social relations. However, their combinations are genera

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