Recommender systems use algorithms to provide users with product or service
recommendations. Recently, these systems have been using machine learning algorithms …

This study aims to implement a hybrid ensemble surrogate machine learning technique in
predicting the compressive strength (CS) of concrete, an important parameter used for …

“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

Automatic Web-service selection is an important research topic in the domain of service
computing. During this process, reliable predictions for quality of service (QoS) based on …

Accurately estimating unknown quality-of-service (QoS) data based on historical records of
Web-service invocations is vital for automatic service selection. This work presents an …

This study implements a hybrid ensemble machine learning method for forecasting the rate
of penetration (ROP) of tunnel boring machine (TBM), which is becoming a prerequisite for …

Promoting recommender systems in real-world applications requires deep investigations
with emphasis on their next generation. This survey offers a comprehensive and systematic …

Building energy consumption prediction plays an irreplaceable role in energy resource
management and planning. Continuous improvement in the performance of predictive …

It has long been found that well-configured recommender system ensembles can achieve
better effectiveness than the combined systems separately. Sophisticated approaches have …

In the previous chapters, we discussed three different classes of recommendation methods.
Collaborative methods use the ratings of a community of users in order to make …