Over the past two decades, a large amount of research effort has been devoted to
develo** algorithms that generate recommendations. The resulting research progress has …

The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …

A recommender system (RS) aims to provide personalized recommendations to users for
specific items (eg, music, books). Popular techniques involve content-based (CB) models …

Recommender systems are an important part of the information and e-commerce ecosystem.
They represent a powerful method for enabling users to filter through large information and …

As one of the most successful approaches to building recommender systems, collaborative
filtering (CF) uses the known preferences of a group of users to make recommendations or …

Existing content-based music recommendation systems typically employ a\textit {two-stage}
approach. They first extract traditional audio content features such as Mel-frequency cepstral …

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections
of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in …

This paper presents an overview of the field of recommender systems and describes the
current generation of recommendation methods that are usually classified into the following …

One of the potent personalization technologies powering the adaptive web is collaborative
filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the …