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 …
Categorization of knowledge graph based recommendation methods and benchmark datasets from the perspectives of application scenarios: A comprehensive …
Recommender Systems (RS) are established to deal with the preferences of users to
enhance their experience and interest in innumerable online applications by streamlining …
enhance their experience and interest in innumerable online applications by streamlining …
FISSA: Fusing item similarity models with self-attention networks for sequential recommendation
Sequential recommendation has been a hot research topic because of its practicability and
high accuracy by capturing the sequential information. As deep learning (DL) based …
high accuracy by capturing the sequential information. As deep learning (DL) based …
Unveiling consumer preferences: A two-stage deep learning approach to enhance accuracy in multi-channel retail sales forecasting
In the dynamic and turbulent business environment, sales forecasting for multi-channel
retailers has become increasingly intricate, particularly with the shift from traditional brick …
retailers has become increasingly intricate, particularly with the shift from traditional brick …
Diff4rec: Sequential recommendation with curriculum-scheduled diffusion augmentation
Sequential recommender systems often suffer from performance drops due to the data-
sparsity issue in real-world scenarios. To address this issue, we bravely take advantage of …
sparsity issue in real-world scenarios. To address this issue, we bravely take advantage of …
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 …
Mitigating sentiment bias for recommender systems
Biases and de-biasing in recommender systems (RS) have become a research hotspot
recently. This paper reveals an unexplored type of bias, ie, sentiment bias. Through an …
recently. This paper reveals an unexplored type of bias, ie, sentiment bias. Through an …
Single-user injection for invisible shilling attack against recommender systems
C Huang, H Li - Proceedings of the 32nd ACM International …, 2023 - dl.acm.org
Recommendation systems (RS) are crucial for alleviating the information overload problem.
Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to …
Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to …
Dual-task learning for multi-behavior sequential recommendation
Recently, sequential recommendation has become a research hotspot while multi-behavior
sequential recommendation (MBSR) that exploits users' heterogeneous interactions in …
sequential recommendation (MBSR) that exploits users' heterogeneous interactions in …
Leveraging multiple features for document sentiment classification
Sentiment classification is an important research task in Natural Language Processing. To
fulfill this type of classification, previous works have focused on leveraging task-specific …
fulfill this type of classification, previous works have focused on leveraging task-specific …