Deep Learning for Recommender Systems
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 60
Special Issue Editors
Interests: data mining; artificial intelligence
Interests: artificial intelligence; information security; adversarial attacks; recommendation systems; big data and distributed computing
Special Issue Information
Dear Colleagues,
Personalized recommendation techniques are widely used in many online scenarios such as e-commerce, education, social networking and news feeds to alleviate users’ information overload. Recommender systems effectively filter and screen information to help users retrieve information resources that meet their needs in a personalized way. After collaborative filtering algorithms were proposed, recommendation systems gradually became a new research hotspot, and also faced problems such as data sparsity and cold starts. Deep learning has the ability to recognize, analyze and compute, and can automatically learn different levels of expression and abstraction of features from data, an effective strategy to effectively solve the problems of cold starts and data sparsity in traditional recommendation technology.
In recent years, deep learning techniques have been widely used in recommender systems, which not only can accurately obtain the relationships between users and items in diverse data, but also can transform abstract codes into high-level data information. Compared with traditional recommendation methods, deep-neural-network-based recommendation has the ability to extract complex user-item features using nonlinear activation functions such as tanh, sigmoid, etc. In addition, deep-neural-network-based recommendation models can incorporate heterogeneous and multivariate data such as highlighting, video, audio, etc., and have stronger data mining capabilities.
The goal of this Special Issue is to provide an overview of the latest developments regarding recommender systems. Both theoretical and technical aspects are of interest. Interdisciplinary approaches are also highly welcome.
Topics of interest include but are not limited to the following:
- Application scenarios for recommender systems;
- Deep learning in recommender systems;
- Recommender systems based on deep neural networks, recurrent neural networks, convolutional neural networks, and graph neural networks;
- Methods to test the robustness of recommender systems;
- Recommender system adversarial attack and defense;
- Security and privacy assessment for recommender systems;
- Novel and diverse metrics for recommender system evaluation;
- Recommender systems for dynamic information scenarios;
- Security awareness and user privacy protection for recommender systems;
- Federated learning in recommender systems.
Dr. Rong Pan
Dr. Mingxing Duan
Dr. Huizhang Luo
Guest Editors
Manuscript Submission Information
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Keywords
- recommender system
- deep learning
- security
- adversarial attack
- federated learning
- robustness
- privacy
