Ranking aggregation (RA), the process of aggregating multiple rankings derived from
multiple search strategies, has been proved effective in person re-identification (re-ID) …

The aim of a recommender system is to suggest to the user certain products or services that
most likely will interest them. Within the context of personalized recommender systems, a …

Recommender systems are one of the most successful applications of using AI for providing
personalized e-services to customers. However, data sparsity is presenting enormous …

Recent recommender systems have shown remarkable performance by using an ensemble
of heterogeneous models. However, it is exceedingly costly because it requires resources …

Ranking ensemble is a critical component in real recommender systems. When a user visits
a platform, the system will prepare several item lists, each of which is generally from a single …

Performance measurement is a complex but important task required in all sectors. The
problem however arises when usage of different methods for performance assessment …

The ranking aggregation problem is gaining attention in different application fields due to its
connection with decision making. One of the most famous ranking aggregation methods can …

In recent years, recommender systems have achieved remarkable performance by using
ensembles of heterogeneous models. However, this approach is costly due to the resources …

Rank fusion is a technique for combining multiple rankings into a single aggregated ranking,
commonly used in high-stakes applications. For hiring decisions, a fused ranking might …

Many recommenders for modelling tasks have recently appeared. They use a variety of
recommendation methods, tailored to concrete modelling languages. Typically …