Learning effective user representations from sequential user-item interactions is a
fundamental problem for recommender systems (RS). Recently, several unsupervised …

Recent years have witnessed great success in deep learning-based sequential
recommendation (SR), which can provide more timely and accurate recommendations. One …

The implicit feedback based collaborative filtering (CF) has attracted much attention in
recent years, mainly because users implicitly express their preferences in many real-world …

One-hot encoder accompanied by a softmax loss has become the default configuration to
deal with the multiclass problem, and is also prevalent in deep learning (DL) based …

Data bias, eg, popularity impairs the dynamics of two-sided markets within recommender
systems. This overshadows the less visible but potentially intriguing long-tail items that could …

There have been many research efforts aimed at improving recommendation accuracy with
Collaborative Filtering (CF). Yet there is still a lack of investigation into the integration of CF …

To address the sparsity problem, a novel collaborative filtering approach based on multiple
attribute decision making (MADM-CF) is proposed. In MADM-CF, users in collaborative …

Artificial intelligence was recently implemented in various fields. One of these applied
artificial intelligence systems was the Performance Monitoring System “SiMoniK” for MSMEs …

Recommender systems (RSs) have been extensively developed to provide personalized
recommendations for the end users from the near-infinite options on the internet. Accuracy is …

In this work, a novel data-driven distributed consensus filter (DD-DCF) is proposed based on
the dynamic linearization technique (DLT) for a discrete-time nonlinear sensor network …