CS229 Project Report Offline Music Recommendation

CS229 Project: Segmenting Music into Notes using Mixture of Gaussians

CS229 Project: Music Search Engine

With the abundance of digital music files on the internet, how to efficiently and effectively find a music piece is crucial. Conventional music search systems utilize text information of the music, such as the title of a song, a singer’s name, or the lyrics of a song. In many cases, such text-based music search tool is not sufficient. Often, a user may remember how to sing part of a song he/she...

Music Recommenders: User Evaluation Without Real Users?

Good music recommenders should not only suggest quality recommendations, but should also allow users to discover new/niche music. User studies capture explicit feedback on recommendation quality and novelty, but can be expensive, and may have difficulty replicating realistic scenarios. Lack of effective offline evaluation methods restricts progress in music recommendation research. The challeng...

A Comparison of Playlist Generation Strategies for Music Recommendation and a New Baseline Scheme

The digitalization of music and the instant availability of millions of tracks on the Internet require new approaches to support the user in the exploration of these huge music collections. One possible approach to address this problem, which can also be found on popular online music platforms, is the use of user-created or automatically generated playlists (mixes). The automated generation of ...

CS221 Project Final Report Community Detection based on Music Listening Habits

Music plays a big role in our lives and forms a billion dollar industry. A lot of research goes into music recommendation and prediction especially with so many internet radio services coming up like Spotify, Pandora etc. Even though community detection, in general, is a well tackled problem in social and information flow networks, an interesting and fairly unsolved problem is that of identifyi...