215 research outputs found
Mining and Predicting Smart Device User Behavior
Three types of user behavior are mined in this paper: application usage, smart device usage and periodicity of user behavior. When mining application usage, the application installation, most frequently used applications and application correlation are analyzed. The application usage is long-tailed. When mining the device usage, the mean, variance and autocorrelation are calculated both for duration and interval. Both the duration and interval are long-tailed but only duration satisfies power-law distribution. Meanwhile, the autocorrelation of both duration and interval is weak, which makes predicting user behavior based on adjacent behavior not so reasonable in related works. Then DFT (Discrete Fourier Transform) is utilized to analyze the periodicity of user behavior and results show that the most obvious periodicity is 24 hours, which is in agreement with related works. Based on the results above, an improved user behavior predicting model is proposed based on Chebyshev inequality. Experiment results show that the performance is good in accurate rate and recall rate
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Evolution mechanism of principal modes in climate dynamics
Eigen analysis has been a powerful tool to distinguish multiple processes into different simple principal modes in complex systems. For a non-equilibrium system, the principal modes corresponding to the non-equilibrium processes are usually evolving with time. Here, we apply the eigen analysis into the complex climate systems. In particular, based on the daily surface air temperature in the tropics (30? S–30? N, 0? E–360? E) between 1979-01-01 and 2016-12-31, we uncover that the strength of two dominated intra-annual principal modes represented by the eigenvalues significantly changes with the El Niño/southern oscillation from year to year. Specifically, according to the ‘regional correlation’ introduced for the first intra-annual principal mode, we find that a sharp positive peak of the correlation between the El Niño region and the northern (southern) hemisphere usually signals the beginning (end) of the El Niño. We discuss the underlying physical mechanism and suppose that the evolution of the first intra-annual principal mode is related to the meridional circulations; the evolution of the second intra-annual principal mode responds positively to the Walker circulation. Our framework presented here not only facilitates the understanding of climate systems but also can potentially be used to study the dynamical evolution of other natural or engineering complex systems. © 2020 The Author(s)
Untargeted Black-box Attacks for Social Recommendations
The rise of online social networks has facilitated the evolution of social
recommender systems, which incorporate social relations to enhance users'
decision-making process. With the great success of Graph Neural Networks in
learning node representations, GNN-based social recommendations have been
widely studied to model user-item interactions and user-user social relations
simultaneously. Despite their great successes, recent studies have shown that
these advanced recommender systems are highly vulnerable to adversarial
attacks, in which attackers can inject well-designed fake user profiles to
disrupt recommendation performances. While most existing studies mainly focus
on targeted attacks to promote target items on vanilla recommender systems,
untargeted attacks to degrade the overall prediction performance are less
explored on social recommendations under a black-box scenario. To perform
untargeted attacks on social recommender systems, attackers can construct
malicious social relationships for fake users to enhance the attack
performance. However, the coordination of social relations and item profiles is
challenging for attacking black-box social recommendations. To address this
limitation, we first conduct several preliminary studies to demonstrate the
effectiveness of cross-community connections and cold-start items in degrading
recommendations performance. Specifically, we propose a novel framework
Multiattack based on multi-agent reinforcement learning to coordinate the
generation of cold-start item profiles and cross-community social relations for
conducting untargeted attacks on black-box social recommendations.
Comprehensive experiments on various real-world datasets demonstrate the
effectiveness of our proposed attacking framework under the black-box setting.Comment: Preprint. Under revie
Enhancing Graph Collaborative Filtering via Uniformly Co-Clustered Intent Modeling
Graph-based collaborative filtering has emerged as a powerful paradigm for
delivering personalized recommendations. Despite their demonstrated
effectiveness, these methods often neglect the underlying intents of users,
which constitute a pivotal facet of comprehensive user interests. Consequently,
a series of approaches have arisen to tackle this limitation by introducing
independent intent representations. However, these approaches fail to capture
the intricate relationships between intents of different users and the
compatibility between user intents and item properties.
To remedy the above issues, we propose a novel method, named uniformly
co-clustered intent modeling. Specifically, we devise a uniformly contrastive
intent modeling module to bring together the embeddings of users with similar
intents and items with similar properties. This module aims to model the
nuanced relations between intents of different users and properties of
different items, especially those unreachable to each other on the user-item
graph. To model the compatibility between user intents and item properties, we
design the user-item co-clustering module, maximizing the mutual information of
co-clusters of users and items. This approach is substantiated through
theoretical validation, establishing its efficacy in modeling compatibility to
enhance the mutual information between user and item representations.
Comprehensive experiments on various real-world datasets verify the
effectiveness of the proposed framework.Comment: In submissio
Automated Machine Learning for Deep Recommender Systems: A Survey
Deep recommender systems (DRS) are critical for current commercial online
service providers, which address the issue of information overload by
recommending items that are tailored to the user's interests and preferences.
They have unprecedented feature representations effectiveness and the capacity
of modeling the non-linear relationships between users and items. Despite their
advancements, DRS models, like other deep learning models, employ sophisticated
neural network architectures and other vital components that are typically
designed and tuned by human experts. This article will give a comprehensive
summary of automated machine learning (AutoML) for developing DRS models. We
first provide an overview of AutoML for DRS models and the related techniques.
Then we discuss the state-of-the-art AutoML approaches that automate the
feature selection, feature embeddings, feature interactions, and system design
in DRS. Finally, we discuss appealing research directions and summarize the
survey
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