Background: The use of social media for disseminating health care information has become
increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine …

Recently, recommender systems have played an increasingly important role in a wide
variety of commercial applications to help users find favourite products. Research in the …

Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …

Ranking algorithms are deployed widely to order a set of items in applications such as
search engines, news feeds, and recommendation systems. Recent studies, however, have …

Novelty and diversity have been identified, along with accuracy, as prominent properties of
useful recommendations. Considerable progress has been made in the field in terms of the …

Abstract Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to
the users, but they are oblivious to their impact on the ranked items. However, there has …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

Ranking is a core task in recommender systems, which aims at providing an ordered list of
items to users. Typically, a ranking function is learned from the labeled dataset to optimize …

Learning to rank for Information Retrieval (IR) is a task to automatically construct a ranking
model using training data, such that the model can sort new objects according to their …

An introduction to information retrieval, the foundation for modern search engines, that
emphasizes implementation and experimentation. Information retrieval is the foundation for …