Music recommendation algorithms based on knowledge graph and multi-task feature learning.

ABSTRACT: During music recommendation scenarios, sparsity and cold start problems are inevitable. Auxiliary information has been utilized in music recommendation algorithms to provide users with more accurate music recommendation results. This study proposes an end-to-end framework, MMSS_MKR, that uses a knowledge graph as a source of auxiliary information to serve the information obtained from it to the recommendation module. The framework exploits Cross & Compression Units to bridge the knowledge graph embedding task with recommendation task modules. We can obtain more realistic triple information and exclude false triple information as much as possible, because our model obtains triple information through the music knowledge graph, and the information obtained through the recommendation module is

SUBMITTER: Liu X 

PROVIDER: S-EPMC10808181 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature