Learning latent relations for temporal knowledge graph reasoning
Abstract Temporal Knowledge Graph (TKG) reasoning aims to predict future facts based on
historical data. However, due to the limitations in construction tools and data sources, many …
historical data. However, due to the limitations in construction tools and data sources, many …
Automl for deep recommender systems: A survey
Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …
in different scenarios, such as e-commerce and social media. With the prosperity of deep …
Evidence-aware fake news detection with graph neural networks
The prevalence and perniciousness of fake news has been a critical issue on the Internet,
which stimulates the development of automatic fake news detection in turn. In this paper, we …
which stimulates the development of automatic fake news detection in turn. In this paper, we …
Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction
Click-through rate (CTR) prediction is an essential task in web applications such as online
advertising and recommender systems, whose features are usually in multi-field form. The …
advertising and recommender systems, whose features are usually in multi-field form. The …
Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations
In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …
received a lot of attention in the past few years and surpassed traditional models such as …
Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network
Session-based recommendation (SBR) aims to predict the user's next action based on short
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …
DGCN: Diversified recommendation with graph convolutional networks
These years much effort has been devoted to improving the accuracy or relevance of the
recommendation system. Diversity, a crucial factor which measures the dissimilarity among …
recommendation system. Diversity, a crucial factor which measures the dissimilarity among …
Contrastive cross-domain sequential recommendation
Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions
based on user's historical sequential interactions from multiple domains. Generally, a key …
based on user's historical sequential interactions from multiple domains. Generally, a key …
Adapting large language models by integrating collaborative semantics for recommendation
Recently, large language models (LLMs) have shown great potential in recommender
systems, either improving existing recommendation models or serving as the backbone …
systems, either improving existing recommendation models or serving as the backbone …
Core: simple and effective session-based recommendation within consistent representation space
Session-based Recommendation (SBR) refers to the task of predicting the next item based
on short-term user behaviors within an anonymous session. However, session embedding …
on short-term user behaviors within an anonymous session. However, session embedding …