2,096 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Generative Adversarial Learning for Intelligent Trust Management in 6G Wireless Networks
Emerging six generation (6G) is the integration of heterogeneous wireless
networks, which can seamlessly support anywhere and anytime networking. But
high Quality-of-Trust should be offered by 6G to meet mobile user expectations.
Artificial intelligence (AI) is considered as one of the most important
components in 6G. Then AI-based trust management is a promising paradigm to
provide trusted and reliable services. In this article, a generative
adversarial learning-enabled trust management method is presented for 6G
wireless networks. Some typical AI-based trust management schemes are first
reviewed, and then a potential heterogeneous and intelligent 6G architecture is
introduced. Next, the integration of AI and trust management is developed to
optimize the intelligence and security. Finally, the presented AI-based trust
management method is applied to secure clustering to achieve reliable and
real-time communications. Simulation results have demonstrated its excellent
performance in guaranteeing network security and service quality
Domain Adaptation with Incomplete Target Domains
Domain adaptation, as a task of reducing the annotation cost in a target
domain by exploiting the existing labeled data in an auxiliary source domain,
has received a lot of attention in the research community. However, the
standard domain adaptation has assumed perfectly observed data in both domains,
while in real world applications the existence of missing data can be
prevalent. In this paper, we tackle a more challenging domain adaptation
scenario where one has an incomplete target domain with partially observed
data. We propose an Incomplete Data Imputation based Adversarial Network
(IDIAN) model to address this new domain adaptation challenge. In the proposed
model, we design a data imputation module to fill the missing feature values
based on the partial observations in the target domain, while aligning the two
domains via deep adversarial adaption. We conduct experiments on both
cross-domain benchmark tasks and a real world adaptation task with imperfect
target domains. The experimental results demonstrate the effectiveness of the
proposed method
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