A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems
Recommender systems are intelligent programs to suggest relevant contents to users
according to their interests which are widely expressed as numerical ratings. Collaborative …
according to their interests which are widely expressed as numerical ratings. Collaborative …
A survey on data mining techniques in recommender systems
Recommender systems have been regarded as gaining a more significant role with the
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …
Cross domain recommendation using multidimensional tensor factorization
In the era of social media, exponential growth of information generated by online social
media and e-commerce applications demands expert and intelligent recommendation …
media and e-commerce applications demands expert and intelligent recommendation …
Cold-start recommendation with provable Guarantees: A decoupled approach
Although the matrix completion paradigm provides an appealing solution to the collaborative
filtering problem in recommendation systems, some major issues, such as data sparsity and …
filtering problem in recommendation systems, some major issues, such as data sparsity and …
New perspectives on gray sheep behavior in E-commerce recommendations
With the exponential rise in the size of data being generated, personalization based on
recommender systems has become an important aspect of digital marketing strategy of E …
recommender systems has become an important aspect of digital marketing strategy of E …
Customer reviews analysis with deep neural networks for e-commerce recommender systems
BM Shoja, N Tabrizi - IEEE access, 2019 - ieeexplore.ieee.org
An essential prerequisite of an effective recommender system is providing helpful
information regarding users and items to generate high-quality recommendations. Written …
information regarding users and items to generate high-quality recommendations. Written …
An lstm based system for prediction of human activities with durations
Human activity prediction is an interesting problem with a wide variety of applications like
intelligent virtual assistants, contextual marketing, etc. One formulation of this problem is …
intelligent virtual assistants, contextual marketing, etc. One formulation of this problem is …
Deep learning approaches for fashion knowledge extraction from social media: a review
Fashion knowledge encourages people to properly dress and faces not only physiological
necessity of users, but also the requirement of social practices and activities. It usually …
necessity of users, but also the requirement of social practices and activities. It usually …
Auric: using data-driven recommendation to automatically generate cellular configuration
Cellular service providers add carriers in the network in order to support the increasing
demand in voice and data traffic and provide good quality of service to the users. Addition of …
demand in voice and data traffic and provide good quality of service to the users. Addition of …
A topological perspective on demystifying gnn-based link prediction performance
Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for
link prediction (LP). While numerous studies aim to improve the overall LP performance of …
link prediction (LP). While numerous studies aim to improve the overall LP performance of …