Deep learning-based collaborative filtering recommender systems: A comprehensive and systematic review
Nowadays, the volume of online information is growing and it is difficult to find the required
information. Effective strategies such as recommender systems are required to overcome …
information. Effective strategies such as recommender systems are required to overcome …
What rating they will probably give: A cognitive diagnosis approach for recommending items based on polytomous responses and latent attributes
FT Marana, R da Silva Fernandes, JLB Guzmán… - Expert Systems with …, 2023 - Elsevier
Recommendation Systems have become prevalent in recent years, attracting the attention of
researchers to investigate different methods to filter relevant information for users. This …
researchers to investigate different methods to filter relevant information for users. This …
Multi-factor ranking method for trading-off accuracy, diversity, novelty, and coverage of recommender systems
Collaborative filtering (CF) is one of the most popular and commonly used recommendation
methods. Currently, most rating prediction CF methods select top-N recommendations …
methods. Currently, most rating prediction CF methods select top-N recommendations …
Multi-criteria ranking: Next generation of multi-criteria recommendation framework
Recommender systems have been developed to assist decision making by recommending a
list of items to the end users. The multi-criteria recommender system (MCRS) is a special …
list of items to the end users. The multi-criteria recommender system (MCRS) is a special …
Hybrid gated recurrent unit and convolutional neural network-based deep learning mechanism for efficient shilling attack detection in social networks
The degree of openness of the socially aware recommendation systems and the possibility
of the attackers injecting vast numbers of fake profiles biases the prediction of the system …
of the attackers injecting vast numbers of fake profiles biases the prediction of the system …
[HTML][HTML] Personalized neural network-based aggregation function in multi-criteria collaborative filtering
Modeling an effective aggregation function to improve the accuracy of recommendations
remains an issue in model-based multi-criteria collaborative filtering (MCCF). The total …
remains an issue in model-based multi-criteria collaborative filtering (MCCF). The total …
Developing a mathematical model of the co-author recommender system using graph mining techniques and big data applications
Finding the most suitable co-author is one of the most important ways to conduct effective
research in scientific fields. Data science has contributed to achieving this possibility …
research in scientific fields. Data science has contributed to achieving this possibility …
Integrating user-side information into matrix factorization to address data sparsity of collaborative filtering
Recommendation techniques play a vital role in recommending an actual product to an
intended user. The recommendation also supports the user in the decision-making process …
intended user. The recommendation also supports the user in the decision-making process …
A Comparative Study of Preference Ordering Methods for Multi-Criteria Ranking
Multi-criteria recommender systems are capable of enhancing recommendation quality by
taking into account user preferences across multiple criteria. A promising approach that has …
taking into account user preferences across multiple criteria. A promising approach that has …
Pruning strategy on adaptive rule model by sorting utility items
The adaptive Rule Model is an association rule development that formulates a minimum
threshold value according to the data characteristics. The formulation process is based on …
threshold value according to the data characteristics. The formulation process is based on …