This chapter gives an introduction to music recommender systems research. We highlight
the distinctive characteristics of music, as compared to other kinds of media. We then …

Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very
good performance for top-N recommendation; however they only estimate a single model for …

We present a simple and scalable algorithm for top-N recommendation able to deal with
very large datasets and (binary rated) implicit feedback. We focus on memory-based …

Since, people are listening to different songs nowadays, selecting an appropriate
recommendation algorithms is remaining as a bottleneck in several music fields. In this …

Academic search engines have been widely used to access academic papers, where users'
information needs are explicitly represented as search queries. Some modern recommender …

Despite the prevalence of collaborative filtering in recommendation systems, there has been
little theoretical development on why and how well it works, especially in the``online''setting …

Even though people are nowadays listening all types of songs, still algorithms are struggling
in many fields. With less historic figures, how does the system know the listeners like a new …

(57) ABSTRACT A two-dimensional matrix of data points represents occur rences of users
selecting content items, such as audio tracks. A server computes n-dimensional latent …

Current top-N recommendation methods compute the recommendations by taking into
account only relations between pairs of items, thus leading to potential unused information …

Top-N recommendations have been studied extensively. Promising results have been
achieved by recent item-based collaborative filtering (ICF) methods. The key to ICF lies in …