Spotify-ed - Music recommendation and discovery in Spotify - José Lage Bateira (2014)
Not so long ago, before the Internet boom, listening or discovering new music was a challenge on its own. Now, with a few clicks one can have on their hands such a vast music catalogue that a human mind not able to compute. There are dozens of online services that offer exactly that. Some focus on creation/generation of playlists, others try to expand their music catalogue even further, but others focus more on personalized music recommendation. And these ones present their results to the user with a list or a grid of music artists, for example. However, lists or grids do not give the user enough information about the relation between the results. One could even say that they are not related to each other, which is not true. The relations exist and can be represented as a network of interconnected artists in a graph, where a node is a music artist, and each edge between them represents a strong connection. This is the concept that RAMA (Relational Artist MAps), a project developed at INESC Porto, uses. From a single search, RAMA is able to draw a graph that helps the user to explore new music that might caught his/her interest in a much more natural way. Nonetheless, when a user wants to listen to an artist's music, Youtube's stream is used. Although one can find a large catalogue of music in Youtube, this service is not Music Oriented and the sound quality is not adequate for a music streaming service. Youtube's stream needs to be replaced, and Spotify can provide a quality stream and an accurate music catalogue. But how can RAMA and Spotify be integrated? This thesis proposes a Spotify App. Will a Spotify user experience a more pleasant and natural way of music discovery from this graphical representation of artist relations within Spotify, than its standard discovery more (with grids)? That is the main question that this dissertation urges to answer.