Common Artist Music Assistance
This addresses the challenge of finding relevant music for individual users in a growing song database, but it is incremental as it builds on existing recommendation techniques.
The paper tackled the problem of personalized music recommendation by developing a system for users with common-artist listening patterns, using a random walk with restart algorithm and optimizing parameters through experiments.
In today's world of growing number of songs, the need of finding apposite music content according to a user's interest is crucial. Furthermore, recommendations suitable to one user may be irrelevant to another. In this paper, we propose a recommendation system for users with common-artist music listening patterns. We use "random walk with restart" algorithm to get relevant recommendations and conduct experiments to find the optimal values of multiple parameters.