A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
Deep rating and review neural network for item recommendation
To alleviate the sparsity issue, many recommender systems have been proposed to
consider the review text as the auxiliary information to improve the recommendation quality …
consider the review text as the auxiliary information to improve the recommendation quality …
What makes a review a reliable rating in recommender systems?
The way that users provide feedback on items regarding their satisfaction varies among
systems: in some systems, only explicit ratings can be entered; in other systems textual …
systems: in some systems, only explicit ratings can be entered; in other systems textual …
[HTML][HTML] Exploiting deep transformer models in textual review based recommender systems
Textual reviews contain fine-grained information that can effectively infer user preferences
over the items. Accordingly, the latest studies in the field of recommender systems exploit …
over the items. Accordingly, the latest studies in the field of recommender systems exploit …
Adversarial machine learning in recommender systems (aml-recsys)
Recommender systems (RS) are an integral part of many online services aiming to provide
an enhanced user-oriented experience. Machine learning (ML) models are nowadays …
an enhanced user-oriented experience. Machine learning (ML) models are nowadays …
Attribute-based neural collaborative filtering
The core task of recommendation systems is to capture user preferences for items. Dot
product operations are usually used to mine user preferences for items. However, the dot …
product operations are usually used to mine user preferences for items. However, the dot …
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 …
Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation
With the rapid development of Internet and Web techniques, Cross-Domain
Recommendation (CDR) models have been widely explored for resolving the data-sparsity …
Recommendation (CDR) models have been widely explored for resolving the data-sparsity …