TY - GEN
T1 - Lyric-based music recommendation
AU - Gossi, Derek
AU - Gunes, Mehmet H.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Traditional music recommendation systems rely on collaborative filtering to recommend songs or artists. This is computationally efficient and performs well method but is not effective when there is limited or no user input. For these cases, it may be useful to consider content-based recommendation. This paper considers a content-based recommendation system based on lyrical data. We compare a complex network of lyrical recommendations to an equivalent collaborative filtering network. We used user generated tag data from Last.fm to produce 23 subgraphs of each network based on tag categories representing musical genre, mood, and gender of vocalist. We analyzed these subgraphs to determine how recommendations within each network tend to stay within tag categories. Finally, we compared the lyrical recommendations to the collaborative filtering recommendations to determine how well lyrical recommendations perform. We see that the lyrical network is significantly more clustered within tag categories than the collaborative filtering network, particularly within small musical niches, and recommendations based on lyrics alone perform 12.6 times better than random recommendations.
AB - Traditional music recommendation systems rely on collaborative filtering to recommend songs or artists. This is computationally efficient and performs well method but is not effective when there is limited or no user input. For these cases, it may be useful to consider content-based recommendation. This paper considers a content-based recommendation system based on lyrical data. We compare a complex network of lyrical recommendations to an equivalent collaborative filtering network. We used user generated tag data from Last.fm to produce 23 subgraphs of each network based on tag categories representing musical genre, mood, and gender of vocalist. We analyzed these subgraphs to determine how recommendations within each network tend to stay within tag categories. Finally, we compared the lyrical recommendations to the collaborative filtering recommendations to determine how well lyrical recommendations perform. We see that the lyrical network is significantly more clustered within tag categories than the collaborative filtering network, particularly within small musical niches, and recommendations based on lyrics alone perform 12.6 times better than random recommendations.
UR - http://www.scopus.com/inward/record.url?scp=84961178850&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961178850&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-30569-1_23
DO - 10.1007/978-3-319-30569-1_23
M3 - Conference contribution
AN - SCOPUS:84961178850
SN - 9783319305684
T3 - Studies in Computational Intelligence
SP - 301
EP - 310
BT - Complex Networks VII - Proceedings of the 7th Workshop on Complex Networks CompleNet 2016
A2 - Cherifi, Hocine
A2 - Goncalves, Bruno
A2 - Menezes, Ronaldo
A2 - Sinatra, Roberta
T2 - 7th Workshop on Complex Networks CompleNet, 2016
Y2 - 23 March 2016 through 25 March 2016
ER -