A tutorial on distance metric learning: Mathematical foundations, algorithms, experimental analysis, prospects and challenges
Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …
the data, which enhances the performance of similarity-based algorithms. This tutorial …
A survey of graph neural networks for social recommender systems
Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …
interactions as well as the user-to-user social relations for the task of generating item …
Personalized digital marketing recommender engine
E-business leverages digital channels to scale its functions and services and operates by
connecting and retaining customers using marketing initiatives. To increase the likelihood of …
connecting and retaining customers using marketing initiatives. To increase the likelihood of …
Community detection in social recommender systems: a survey
Abstract Information extracted from social network services promise to improve the accuracy
of recommender systems in various domains. Against this background, community detection …
of recommender systems in various domains. Against this background, community detection …
Reciprocal Recommender Systems: Analysis of state-of-art literature, challenges and opportunities towards social recommendation
There exist situations of decision-making under information overload in the Internet, where
people have an overwhelming number of available options to choose from, eg products to …
people have an overwhelming number of available options to choose from, eg products to …
A privacy-preserving distributed contextual federated online learning framework with big data support in social recommender systems
Nowadays, the booming demand of big data analytics and the constraints of computational
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …
ability and network bandwidth have made it difficult for a stand-alone agent/service provider …
A comprehensive analysis on movie recommendation system employing collaborative filtering
Collaborative Filtering (CF) is one of the most extensively used technologies for
Recommender Systems (RS), it shows an improved intelligent searching mechanism for …
Recommender Systems (RS), it shows an improved intelligent searching mechanism for …
A survey on recommender systems for Internet of Things: techniques, applications and future directions
Recommendation is a critical tool for developing and promoting the benefits of the Internet of
Things (IoT). In recent years, recommender systems have attracted considerable attention in …
Things (IoT). In recent years, recommender systems have attracted considerable attention in …
[HTML][HTML] Creating personalized recommendations in a smart community by performing user trajectory analysis through social internet of things deployment
Despite advancements in the Internet of Things (IoT) and social networks, developing an
intelligent service discovery and composition framework in the Social IoT (SIoT) domain …
intelligent service discovery and composition framework in the Social IoT (SIoT) domain …
A parameter-free KNN for rating prediction
Among the most popular collaborative filtering algorithms are methods based on the K
nearest neighbors (KNN). In their basic operation, KNN methods consider a fixed number of …
nearest neighbors (KNN). In their basic operation, KNN methods consider a fixed number of …