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 …

Collaborative filtering has been the most straightforward and most preferable approach in
the recommender systems. This technique recommends an item to a target user from the …

Item-based filtering technique is a collaborative filtering algorithm for recommendations.
Correlation-based similarity measures such as cosine similarity, Pearson correlation, and its …

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

To improve the effectiveness of online learning, the learning materials recommendation is
required to be personalised to the learner material recommendations must be personalized …

In the era of Big Data, a massive amount of data is generated and collected, continually,
from various sources. To use these data for their optimum value, ie to uncover correlations …

The exponential increase in digital data has increased the amount of available online
information. This complicates the user's decision-making. Most online merchants and …

The most popular method collaborative filter approach is primarily used to handle the
information overloading problem in E-Commerce. Traditionally, collaborative filtering uses …

The most important subjects in the memory‐based collaborative filtering recommender
system (RS) are to accurately calculate the similarities between users and finally finding …

Collaborative filtering is a popular recommender system (RecSys) method that produces
rating prediction values for products by combining the ratings that close users have already …