Learning social representations with deep autoencoder for recommender system
With the development of online social media, it attracts increasingly attentions to utilize
social information for recommender systems. Based on the intuition that users are influenced …
social information for recommender systems. Based on the intuition that users are influenced …
AR-CF: Augmenting virtual users and items in collaborative filtering for addressing cold-start problems
Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF)
used in recommender systems. When few ratings are available, CF models typically fail to …
used in recommender systems. When few ratings are available, CF models typically fail to …
A personalized clustering-based and reliable trust-aware QoS prediction approach for cloud service recommendation in cloud manufacturing
J Liu, Y Chen - Knowledge-Based Systems, 2019 - Elsevier
With the growing popularity of cloud manufacturing (CMfg), an increasing number of
functionally equivalent cloud services are available on the Internet, which makes cloud …
functionally equivalent cloud services are available on the Internet, which makes cloud …
Deep hybrid recommender systems via exploiting document context and statistics of items
The sparsity of user-to-item rating data is one of the major obstacles to achieving high rating
prediction accuracy of model-based collaborative filtering (CF) recommender systems. To …
prediction accuracy of model-based collaborative filtering (CF) recommender systems. To …
Rating augmentation with generative adversarial networks towards accurate collaborative filtering
Generative Adversarial Networks (GAN) have not only achieved a big success in various
generation tasks such as images, but also boosted the accuracy of classification tasks by …
generation tasks such as images, but also boosted the accuracy of classification tasks by …
Data set quality in machine learning: consistency measure based on group decision making
Abstract Performance of Machine Learning models heavily depends on the quality of the
training dataset. Among others, the quality of training data relies on the consistency of the …
training dataset. Among others, the quality of training data relies on the consistency of the …
A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method
Matrix Factorization (MF) has been proven to be an effective approach for the generation of
a successful recommender system. However, most current MF-based recommenders cannot …
a successful recommender system. However, most current MF-based recommenders cannot …
Sharing notes: An academic social network based on a personalized fuzzy linguistic recommender system
Social networks are Web systems that enable and encourage a collaborative work, making it
possible to exchange information between users, which makes them especially useful in …
possible to exchange information between users, which makes them especially useful in …
A new confidence-based recommendation approach: Combining trust and certainty
Collaborative Filtering (CF) is one of the most successful recommendation techniques.
Recently, implicit trust-based recommendation approaches have emerged that incorporate …
Recently, implicit trust-based recommendation approaches have emerged that incorporate …
Analysing cloud QoS prediction approaches and its control parameters: considering overall accuracy and freshness of a dataset
Service level agreement (SLA) management is one of the key issues in cloud computing.
The primary goal of a service provider is to minimize the risk of service violations, as these …
The primary goal of a service provider is to minimize the risk of service violations, as these …