A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
Embedding Compression in Recommender Systems: A Survey
To alleviate the problem of information explosion, recommender systems are widely
deployed to provide personalized information filtering services. Usually, embedding tables …
deployed to provide personalized information filtering services. Usually, embedding tables …
Graph neural pre-training for recommendation with side information
Leveraging the side information associated with entities (ie, users and items) to enhance
recommendation systems has been widely recognized as an essential modeling dimension …
recommendation systems has been widely recognized as an essential modeling dimension …
Optembed: Learning optimal embedding table for click-through rate prediction
Click-through rate (CTR) prediction model usually consists of three components: embedding
table, feature interaction layer, and classifier. Learning embedding table plays a …
table, feature interaction layer, and classifier. Learning embedding table plays a …
Recommendation systems: An insight into current development and future research challenges
Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …
constantly challenging the current state-of-the-art. Inspired by advancements in many …
Budgeted embedding table for recommender systems
At the heart of contemporary recommender systems (RSs) are latent factor models that
provide quality recommendation experience to users. These models use embedding …
provide quality recommendation experience to users. These models use embedding …
Distill-vq: Learning retrieval oriented vector quantization by distilling knowledge from dense embeddings
Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and
Product Quantization (PQ), have been widely applied to embedding based document …
Product Quantization (PQ), have been widely applied to embedding based document …
Embedding in recommender systems: A survey
Recommender systems have become an essential component of many online platforms,
providing personalized recommendations to users. A crucial aspect is embedding …
providing personalized recommendations to users. A crucial aspect is embedding …
End-to-end learnable clustering for intent learning in recommendation
Intent learning, which aims to learn users' intents for user understanding and item
recommendation, has become a hot research spot in recent years. However, the existing …
recommendation, has become a hot research spot in recent years. However, the existing …
Continuous input embedding size search for recommender systems
Latent factor models are the most popular backbones for today's recommender systems
owing to their prominent performance. Latent factor models represent users and items as …
owing to their prominent performance. Latent factor models represent users and items as …