TY - JOUR

T1 - Mining Users Playbacks History for Music Recommendations

AU - Dzuba, Alexandr

AU - Bugaychenko, Dmitry

PY - 2014

Y1 - 2014

N2 - This paper presents a set of methods for the analysis of user activity and data preparation for the music recommender by the example of “Odnoklassniki” social network. The history of actions is being analyzed in multiple dimensions in order to find a number of collaborative and temporal correlations as well as to make the overall rankings. The results of the analysis are being exported in a form of a taste graph which is then used to generate on-line music recommendations. The taste graph displays relations between different entities connected with music (users, tracks, artists, etc.) and consists of the following main parts: user preferences, track similarities, artists’ similarities, artists’ works and demography profiles.

AB - This paper presents a set of methods for the analysis of user activity and data preparation for the music recommender by the example of “Odnoklassniki” social network. The history of actions is being analyzed in multiple dimensions in order to find a number of collaborative and temporal correlations as well as to make the overall rankings. The results of the analysis are being exported in a form of a taste graph which is then used to generate on-line music recommendations. The taste graph displays relations between different entities connected with music (users, tracks, artists, etc.) and consists of the following main parts: user preferences, track similarities, artists’ similarities, artists’ works and demography profiles.

KW - music recommendations taste graph item similarity

U2 - 10.1007/978-3-319-08979-9_31

DO - 10.1007/978-3-319-08979-9_31

M3 - Article

VL - 8556

SP - 422

EP - 430

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

ER -