Structure-oriented prediction in complex networks
Complex systems are extremely hard to predict due to its highly nonlinear interactions and
rich emergent properties. Thanks to the rapid development of network science, our …
rich emergent properties. Thanks to the rapid development of network science, our …
Movie collaborative filtering with multiplex implicit feedbacks
Movie recommender systems have been widely used in a variety of online networking
platforms to give users reasonable advice from a large number of choices. As a …
platforms to give users reasonable advice from a large number of choices. As a …
Robust non-negative matrix factorization for link prediction in complex networks using manifold regularization and sparse learning
G Chen, C Xu, J Wang, J Feng, J Feng - Physica A: Statistical Mechanics …, 2020 - Elsevier
The aim of link prediction is to disclose the underlying evolution mechanism of networks,
which could be utilized to predict missing links or eliminate spurious links. However, real …
which could be utilized to predict missing links or eliminate spurious links. However, real …
Evaluating user reputation in online rating systems via an iterative group-based ranking method
Reputation is a valuable asset in online social lives and it has drawn increased attention.
Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation …
Due to the existence of noisy ratings and spamming attacks, how to evaluate user reputation …
A novel time-aware hybrid recommendation scheme combining user feedback and collaborative filtering
H Li, D Han - IEEE Systems Journal, 2020 - ieeexplore.ieee.org
Nowadays, recommender systems are used widely in various fields to solve the problem of
information overload. Collaborative filtering and content-based are representative solutions …
information overload. Collaborative filtering and content-based are representative solutions …
Link prediction based on non-negative matrix factorization
B Chen, F Li, S Chen, R Hu, L Chen - PloS one, 2017 - journals.plos.org
With the rapid expansion of internet, the complex networks has become high-dimensional,
sparse and redundant. Besides, the problem of link prediction in such networks has also …
sparse and redundant. Besides, the problem of link prediction in such networks has also …
Deep sparse autoencoder prediction model based on adversarial learning for cross-domain recommendations
Y Li, J Ren, J Liu, Y Chang - Knowledge-Based Systems, 2021 - Elsevier
Online recommender systems generally suffer from severe data sparsity problems, and this
are particularly prevalent in newly launched systems that do not have sufficient amounts of …
are particularly prevalent in newly launched systems that do not have sufficient amounts of …
TIIREC: A tensor approach for tag-driven item recommendation with sparse user generated content
In recent years, tagging system has become a building block o summarize the content of
items for further functions like retrieval or personalized recommendation in various web …
items for further functions like retrieval or personalized recommendation in various web …
A trust-based recommendation method using network diffusion processes
LJ Chen, J Gao - Physica A: Statistical Mechanics and its Applications, 2018 - Elsevier
A variety of rating-based recommendation methods have been extensively studied including
the well-known collaborative filtering approaches and some network diffusion-based …
the well-known collaborative filtering approaches and some network diffusion-based …
Collaborative filtering recommendation on users' interest sequences
W Cheng, G Yin, Y Dong, H Dong, W Zhang - PloS one, 2016 - journals.plos.org
As an important factor for improving recommendations, time information has been
introduced to model users' dynamic preferences in many papers. However, the sequence of …
introduced to model users' dynamic preferences in many papers. However, the sequence of …