Privacy-Aware Web APIs Recommendation for Consumer Mashup Creation Based on Iterative Quantification
R Zhang, L Qi, C Yan, Z Chen, W Gong… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
With the emergence of the" unmanned" field, unmanned supermarket software has entered
consumers' lives in line with the pace of development of the times. Nowadays, developers of …
consumers' lives in line with the pace of development of the times. Nowadays, developers of …
Adversarial machine learning: On the resilience of third-party library recommender systems
In recent years, we have witnessed a dramatic increase in the application of Machine
Learning algorithms in several domains, including the development of recommender …
Learning algorithms in several domains, including the development of recommender …
User-centric evaluation of recommender systems: a literature review
K Nanath, M Ahmed - International Journal of Business …, 2023 - inderscienceonline.com
Recommender systems have seen a rapid rise of application in various industries, with
several services now being implemented online. Over the years, various authors have been …
several services now being implemented online. Over the years, various authors have been …
Learning Robust Recommender from Noisy Implicit Feedback
The ubiquity of implicit feedback makes it indispensable for building recommender systems.
However, it does not actually reflect the actual satisfaction of users. For example, in E …
However, it does not actually reflect the actual satisfaction of users. For example, in E …
Barriers for academic data science research in the new realm of behavior modification by digital platforms
The era of behavioral big data has created new avenues for data science research, with
many new contributions stemming from academic researchers. Yet, data controlled by …
many new contributions stemming from academic researchers. Yet, data controlled by …
Privacy‐preserving graph publishing with disentangled variational information bottleneck
L Ma, C Li, S Sun, S Guo, L Wang… - … : Practice and Experience, 2024 - Wiley Online Library
Social networks collect enormous amounts of user personal and behavioral data, which
could threaten users' privacy if published or shared directly. Privacy‐preserving graph …
could threaten users' privacy if published or shared directly. Privacy‐preserving graph …
A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning
Abnormal states in deep reinforcement learning~(RL) are states that are beyond the scope
of an RL policy. Such states may lead to sub-optimal and unsafe decision making for the RL …
of an RL policy. Such states may lead to sub-optimal and unsafe decision making for the RL …
Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples
A Montazeralghaem, J Allan - arXiv preprint arXiv:2303.05575, 2023 - arxiv.org
Conversational recommender systems (CRSs) are improving rapidly, according to the
standard recommendation accuracy metrics. However, it is essential to make sure that these …
standard recommendation accuracy metrics. However, it is essential to make sure that these …
A lightweight metric defence strategy for graph neural networks against poisoning attacks
Y Xiao, J Li, W Su - … Security: 23rd International Conference, ICICS 2021 …, 2021 - Springer
Graph neural networks (GNN) are a specialized type of deep neural networks on graph
structured data by aggregating the learned representations of node neighborhood, which …
structured data by aggregating the learned representations of node neighborhood, which …
Matryoshka attack: research on an attack method of recommender system based on adversarial learning and optimization solution
H Wang, J Zhong - 2020 International Conference on Wavelet …, 2020 - ieeexplore.ieee.org
Currently, recommendation systems have been widely used in various fields, especially
profitable ones. However, the research on the attack method of the recommendation system …
profitable ones. However, the research on the attack method of the recommendation system …