Internet of Things (IoT) has emerged in many industries, such as health care, transportation,
agriculture, manufacturing, smart homes, to name a few. It paves the path for massive …

Dropping fractions of users or items judiciously can reduce the computational cost of
Collaborative Filtering (CF) algorithms. The effect of this subsampling on the computing time …

Recommender systems play an indispensable role in today's online businesses. In these
systems, memory-based (neighborhood-based) collaborative filtering is an important …

The Internet of Things (IoT) connects heterogeneous physical devices with the ability to
collect data using sensors and actuators. These data can infer useful information for …

In recent years, recommendation systems have become more complex with increasing
research on user preferences. Recommendation algorithm based on deep learning has …

Recommendation systems are involved in many industries, for example (e-health,
transportation, e-commerce, and agriculture), where Recommendation systems aim to …

User intention which often changes dynamically is considered to be an important factor for
modeling users in the design of recommendation systems. Recent studies are starting to …

Recommender systems (RS) help users pick the relevant items among numerous items that
are available on the internet. The items may be movies, food, books, etc. The Recommender …

In this paper, we propose a global architecture of a recommender tool, which represents a
part of an existing collaborative platform. This tool provides diagnostic documents for …

The performance of memory-based collaborative filtering recommender systems will be
severely affected when the users' item preference data is sparse. In this paper, we focus on …