Collaborative recommendation approaches based on nearest-neighbors are still highly
popular today due to their simplicity, their efficiency, and their ability to produce accurate and …

Abstract In the Artificial Intelligence (AI) field, and particularly within the area of Machine
Learning (ML), recommender systems have attracted significant research attention. These …

E-commerce systems experience poor quality of performance when the number of records in
the customer database increases due to the gradual growth of customers and products …

The importance of recommendation systems for business applications has led to extensive
research efforts to improve the recommendations accuracy as well as to reduce the sparsity …

Recommender systems are intelligent programs to suggest relevant contents to users
according to their interests which are widely expressed as numerical ratings. Collaborative …

Recommender systems have been regarded as gaining a more significant role with the
emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF …

Items in real-world recommender systems exhibit certain hierarchical structures. Similarly,
user preferences also present hierarchical structures. Recent studies show that …

Identifying the hidden features of items and users of a modern recommendation system,
wherein features are represented as hierarchical structures, allows us to understand the …

We introduce EigenRec, a versatile and efficient latent factor framework for top-N
recommendations that includes the well-known PureSVD algorithm as a special case …

The study of networks has witnessed an explosive growth over the past decades with
several ground-breaking methods introduced. A particularly interesting—and prevalent in …