Abstract: With the accessibility to information, users often face the problem of selecting one item (a product or a service) from a huge search space. This problem is known as information overload.
RL4RS is a real-world deep reinforcement learning recommender system dataset for practitioners and researchers. 09/02/2022: We release RL4RS v1.1.0. 1) two additional RS datasets for comparison, ...
Auto-complete is a key feature for many web services. When you type in some phrases in Google, it presents a list of search suggestions. Sometimes these results use your input as prefix and sometimes ...
Development of a novel deep learning architecture to accurately predict user preferences based on their viewing behavior and patterns. Integration of graph analysis techniques with deep learning to ...
Not every recommendation system relies on what others liked. Sometimes, the best suggestions come from understanding the content itself — be it product descriptions, genres, ingredients, or tags. This ...
In machine learning, particularly in recommendation system design, model architecture and training methods are deeply shaped by application-specific requirements. Two-tower models -- introduced ...
Similarity search is essential in current artificial intelligence applications and widely utilized in various fields, such as recommender systems. However, the exponential growth of data poses ...
Recommender systems are currently applied in many fields. They try to provide users with recommendation services based on their personalized preferences to reduce the ever increasing amount of ...
The reach of Microsoft's super-popular, open source-based Visual Studio Code editor is still expanding, now supplying the tech for two of the most prominent code repository platforms, GitHub and ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results