Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer …
Deep learning (DL) has solved a problem that a few years ago was thought to be intractable— the automatic recognition of patterns in spatial and temporal data with an accuracy superior …
A Da'u, N Salim - Artificial Intelligence Review, 2020 - Springer
These days, many recommender systems (RS) are utilized for solving information overload problem in areas such as e-commerce, entertainment, and social media. Although classical …
Recommender systems have become exceptionally widespread in recent years to deal with the information overload problem by providing personalized recommendations. Multi-criteria …
L Wang, Z Liu, A Liu, F Tao - The International Journal of Advanced …, 2021 - Springer
Recently, artificial intelligence (AI) technology receives extensive attention in the manufacturing field. As the core technology, it generates considerable interest among smart …
W Chen, F Cai, H Chen, MD Rijke - ACM Transactions on Information …, 2019 - dl.acm.org
We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning …
RJ Kuo, SS Li - Applied Soft Computing, 2023 - Elsevier
With the rapid development of electronic commerce, the availability of a large amount of information on the products, as well as from other users, make the customers' decision …
Recommendation services become a critical and hot research topic for researchers. A recommendation agent that automatically suggests products to users according to their …
With the growth of online information, varying personalization drifts and volatile behaviors of internet users, recommender systems are effective tools for information filtering to overcome …