Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems
J Song, Z Li, Z Hu, Y Wu, Z Li, J Li… - 2020 IEEE 36th …, 2020 - ieeexplore.ieee.org
Data-driven recommender systems that can help to predict users' preferences are deployed
in many real online service platforms. Several studies show that they are vulnerable to data …
in many real online service platforms. Several studies show that they are vulnerable to data …
[PDF][PDF] Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks.
Latent factor models have been widely used for recommendation. Most existing latent factor
models mainly utilize the rating information between users and items, although some …
models mainly utilize the rating information between users and items, although some …
Knowledge graph embedding: A survey from the perspective of representation spaces
Knowledge graph embedding (KGE) is an increasingly popular technique that aims to
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …
Recommender systems based on graph embedding techniques: A review
Y Deng - IEEE Access, 2022 - ieeexplore.ieee.org
As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …
predict user's preferred items from millions of candidates by analyzing observed user-item …
Graph neural collaborative topic model for citation recommendation
Due to the overload of published scientific articles, citation recommendation has long been a
critical research problem for automatically recommending the most relevant citations of …
critical research problem for automatically recommending the most relevant citations of …
Openhgnn: an open source toolkit for heterogeneous graph neural network
Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph
representation learning methods on heterogeneous graphs, have attracted increasing …
representation learning methods on heterogeneous graphs, have attracted increasing …
Distilling structured knowledge into embeddings for explainable and accurate recommendation
Recently, the embedding-based recommendation models (eg, matrix factorization and deep
models) have been prevalent in both academia and industry due to their effectiveness and …
models) have been prevalent in both academia and industry due to their effectiveness and …
Multi-task learning for recommendation over heterogeneous information network
Traditional recommender systems (RS) only consider homogeneous data and cannot fully
model heterogeneous information of complex objects and relations. Recent advances in the …
model heterogeneous information of complex objects and relations. Recent advances in the …
A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains
M Gan, OC Kwon - Knowledge-Based Systems, 2022 - Elsevier
Recently, contextual multiarmed bandits (CMAB)-based recommendation has shown
promise for applications in dynamic domains such as news or short video recommendation …
promise for applications in dynamic domains such as news or short video recommendation …
An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation
This paper studies graph-based recommendation, where an interaction graph is built from
historical responses and is leveraged to alleviate data sparsity and cold start problems. We …
historical responses and is leveraged to alleviate data sparsity and cold start problems. We …