Recommender models excel at providing domain-specific item recommendatio...
Sequential user modeling, a critical task in personalized recommender
sy...
Large language models (LLMs) have achieved significant performance in ma...
Dense retrieval is widely used for entity linking to retrieve entities f...
Collaborative Filtering (CF) is a widely used and effective technique fo...
Recent recommender systems have shown remarkable performance by using an...
Graph-based collaborative filtering is capable of capturing the essentia...
The learn-to-compare paradigm of contrastive representation learning (CR...
A large-scale recommender system usually consists of recall and ranking
...
The lack of labeled data is a major obstacle to learning high-quality
se...
User interest exploration is an important and challenging topic in
recom...
Transformer encoding networks have been proved to be a powerful tool of
...
Precise user modeling is critical for online personalized recommendation...
Representation learning on user-item graph for recommendation has evolve...
User modeling is critical for many personalized web services. Many exist...
The latest advance in recommendation shows that better user and item
rep...
Knowledge graph contains well-structured external information and has sh...
Candidate retrieval is a crucial part in recommendation system, where qu...
The success of recommender systems in modern online platforms is insepar...
Combinatorial features are essential for the success of many commercial
...