Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …

Feature quality has an impactful effect on recommendation performance. Thereby, feature
selection is a critical process in developing deep learning-based recommender systems …

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 …

Latent factor models are the driving forces of the state-of-the-art recommender systems, with
an important insight of vectorizing raw input features into dense embeddings. The …

Designing an effective loss function plays a crucial role in training deep recommender
systems. Most existing works often leverage a predefined and fixed loss function that could …

With the fast development of online E-commerce Websites and mobile applications, users'
auxiliary information as well as products' textual information can be easily collected to form a …

Most recommendation algorithms mainly make use of user history interactions in the model,
while these methods often suffer from the cold-start problem (user/item has no history …

Deep recommender systems have achieved promising performance on real-world
recommendation tasks. They typically represent users and items in a low-dimensional …

The advancement in technology accelerated and opened availability of various alternatives
to make a choice in every domain. In the era of big data it is a tedious and time consuming …

With the growing volume of online information, recommender systems have been an
effective strategy to overcome information overload. The utility of recommender systems …