Time-aware recommender systems: a comprehensive survey and quantitative assessment of literature
Recommender systems (RS) are among the most widely used applications in data mining
and machine-learning technologies. These technologies recommend relevant products to …
and machine-learning technologies. These technologies recommend relevant products to …
[HTML][HTML] A novel healthy and time-aware food recommender system using attributed community detection
Food recommendation systems aim to provide recommendations according to a user's diet,
recipes, and preferences. These systems are deemed useful for assisting users in changing …
recipes, and preferences. These systems are deemed useful for assisting users in changing …
Evaluating collaborative filtering recommender algorithms: a survey
Due to the explosion of available information on the Internet, the need for effective means of
accessing and processing them has become vital for everyone. Recommender systems …
accessing and processing them has become vital for everyone. Recommender systems …
A deep learning based trust-and tag-aware recommender system
Recommender systems are popular tools used in many applications, such as e-commerce, e-
learning, and social networks to help users select their desired items. Collaborative filtering …
learning, and social networks to help users select their desired items. Collaborative filtering …
Recommender systems for large-scale social networks: A review of challenges and solutions
Social networks have become very important for networking, communications, and content
sharing. Social networking applications generate a huge amount of data on a daily basis …
sharing. Social networking applications generate a huge amount of data on a daily basis …
Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach
Recommender systems use intelligent algorithms to learn a user's preferences and provide
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …
them relevant suggestions. Lack of sufficient ratings–also known as data sparsity problem …
A deep reinforcement learning based long-term recommender system
L Huang, M Fu, F Li, H Qu, Y Liu, W Chen - Knowledge-based systems, 2021 - Elsevier
Recommender systems aim to maximize the overall accuracy for long-term
recommendations. However, most of the existing recommendation models adopt a static …
recommendations. However, most of the existing recommendation models adopt a static …
[HTML][HTML] A novel healthy food recommendation to user groups based on a deep social community detection approach
Existing food recommendation models have typically suggested foods or recipes to single
users. However, in reality, users may be members of a group, family, or community, requiring …
users. However, in reality, users may be members of a group, family, or community, requiring …
Comparative study of recommender system approaches and movie recommendation using collaborative filtering
The increasing demand for personalized information has resulted in the development of the
Recommender System (RS). RS has been widely utilized and broadly studied to suggest the …
Recommender System (RS). RS has been widely utilized and broadly studied to suggest the …
RDERL: Reliable deep ensemble reinforcement learning-based recommender system
Recommender systems (RSs) have been employed for many real-world applications
including search engines, social networks, and information retrieval systems as powerful …
including search engines, social networks, and information retrieval systems as powerful …