Recommender systems are effective tools of information filtering that are prevalent due to
increasing access to the Internet, personalization trends, and changing habits of computer …

摘要自编码器是深度学习中的一种非常重要的无监督学习方法, 能够从大量无标签的数据中自动
学习, 得到蕴含在数据中的有效特征. 因此, 自编码方法近年来受到了广泛的关注 …

Research about recommender systems emerges over the last decade and comprises
valuable services to increase different companies' revenue. While most existing …

Recommender systems attempt to provide effective suggestions to each user based on their
interests and behaviors. These recommendations usually match the personal user …

Adaptive learning, supported by Information & Communication Technology (TIC), is an
important research area for educational systems which aim to improve the outcomes of …

Accurate tourism demand forecasting is fundamental in the tourism industry, while effective
tourism demand forecasting using search query data (SQD) has become popular in the …

With the ever‐increasing use of Internet and social networks that generate a vast amount of
information, there is a serious need for recommendation systems. In this article, we propose …

In the current digital landscape, both information consumers and producers encounter
numerous challenges, underscoring the importance of recommender systems (RS) as a vital …

Abstract We propose “Deep Autoencoders for Feature Learning in Recommender Systems,”
a novel discriminative model based on the incorporation of features from autoencoders in …

The role of the Internet of Things (IoT) networks and systems in our daily life cannot be
underestimated. IoT is among the fastest evolving innovative technologies that are digitizing …