Today, recommender systems play a vital role in the acceleration of searches by internet
users to find what they are interested in. Among the strategies proposed for recommender …

In the recommendation domain, user rating data has high sparsity and the number of
interaction information from each user is very uneven, which brings great technical …

In music there are various types of genres and every person has their own choice of the type
of music they want to hear. Recommendation system is an important feature in an …

Recommender systems include a broad scope of applications and are associated with
subjective preferences, indicating variations in recommendations. As a field of data science …

(online advertising of products has garnered substantial interest across a variety of
channels, including search engines, third-party websites, social media platforms, and mobile …

In this paper, a methodology based on a Bayesian Network (BN) is proposed to create
reliable corrosion alarm maps of first layer reinforcements, which is related to corrosion …

The great progress in information and communication technology has led to a huge increase
in data available. Traditional systems can't keep up with this growth and can't handle this …

The most influencing factors in recommending a suitable book can be the language it is
written in or the author the book has been written by and majorly, the genre that book belong …

Data sparsity remains to be a critical concern for recommendation systems since it results in
low accuracy and poor recommendation quality. To address this problem, collaborative …

Sažetak Recommender systems are used in various applications to boost the prediction
accuracy of user preferences. The recent developments in recommendation frameworks …