Feature selection is a pre-processing technique used to remove unnecessary
characteristics, and speed up the algorithm's work process. A part of the technique is carried …

A collaborative recommender system based on a latent factor model has achieved
significant success in the field of personalized recommender systems. However, the latent …

In the Internet era, where information and communication technologies (ICT) allow data
exchange, new tools able to select the correct data are needed. In this field, Recommender …

Singular value decomposition (SVD) is one of the most widely used algorithms for
dimensionality reduction and performing principal component analysis, which represents an …

As a social information product, the privacy and usability of high-dimensional data are the
core issues in the field of privacy protection. Feature selection is a commonly used …

Collaborative filtering has become one of the most widely used methods for a variety of
commercial recommendations. The key to collaborative filtering is use similarity calculation …

In this paper, we build a recommender system based on Long-term Cognitive Networks
(LTCNs), which are a type of recurrent neural network that allows reasoning with prior …

To facilitate more effective knowledge utilization in the process of product intelligent design,
aiming at the sparsity and cold start issues in knowledge push, an adaptive push method of …

Collaborative Filtering based on matrix factorization (MF) has shown tremendous success in
the field recommender system. However, MF has difficulty in handling sparsity and …

With the increase in computerisation in every field, a huge amount of data is collected from
everywhere. Therefore, extracting useful information has become a necessary task in the …