Many websites over the Internet are producing a variety of textual data; such as news, research articles, ebooks, personal blogs, and user reviews. In these websites, data is so large that process finding pertinent information by often becomes cumbersome. To overcome this issue, “Text-based Recommendation Systems (RS)” being developed. They systems with capability to find relevant in minimal ti...

Recently the use of web recommendation techniques is growing worldwide with aim of providing the customized required data to end users. Different recommendation methods and solutions impose many research challenges to researchers. Web recommendation techniques are divided into two main types such as content based web recommendation system and collaborative web recommendation system. Basically w...

Recommendation systems are the agents that help the learner to identify a subset of suitable learning resources from a variety of choices. Recommendation Systems is a widely explored field since the last decade. Much of the work is going on in recommendation systems that are based on the evaluation of resources and users‟ data. In this paper we concentrate on E-Learning Recommendation Systems. ...

Most existing recommendation systems (RSs) are primarily concerned about the accuracy of rating prediction and only recommending popular items. However, other non-accuracy metrics such as novelty diversity should not be overlooked. Existing multi-objective (MO) RSs employed collaborative filtering combined with evolutionary algorithms to handle bi-objective optimization. Besides cold-start prob...

With the emergence of personality computing as a new research field related to artificial intelligence and psychology, we have witnessed an unprecedented proliferation personality-aware recommendation systems. Unlike conventional systems, these systems solve traditional problems such cold start data sparsity problems. This survey aims study systematically classify To best our knowledge, this is...