In our work, we have presented two widely used recommendation systems. We have
presented a context-aware recommender system to filter the items associated with user's …

Recommender systems in e-learning domain play an important role in assisting the learners
to find useful and relevant learning materials that meet their learning needs. Personalized …

A recommender system aims to provide users with personalized online product or service
recommendations to handle the increasing online information overload problem and …

In recent years there has been an enormous increase in learning resources available online
through massive open online courses and learning management systems. In this context …

Content-based recommender systems (CBRSs) rely on item and user descriptions (content)
to build item representations and user profiles that can be effectively exploited to suggest …

Semantic Data Mining refers to the data mining tasks that systematically incorporate domain
knowledge, especially formal semantics, into the process. In the past, many research efforts …

Recommender system (RS) has emerged as a major research interest that aims to help
users to find items online by providing suggestions that closely match their interest. This …

The Web has moved, slowly but steadily, from a collection of documents towards a collection
of structured data. Knowledge graphs have then emerged as a way of representing the …

Recommender systems have been widely used in various domains including movies, news,
music with an aim to provide the most relevant proposals to users from a variety of available …

The great quantity of music content available online has increased interest in music
recommender systems. However, some important problems must be addressed in order to …