Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet.

ABSTRACT: Introducing a knowledge graph into a recommender system as auxiliary information can effectively solve the sparse and cold start problems existing in traditional recommender systems. In recent years, many researchers have performed related work. A recommender system with knowledge graph embedding learning characteristics can be combined with a recommender system of the following three forms: one-by-one learning, joint learning, and alternating learning. For current knowledge graph embedding, a deep learning framework only has one embedding mode, which fails to excavate the potential information from the knowledge graph thoroughly. To solve this problem, this paper proposes the Ripp-MKR model, a multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet,

SUBMITTER: Wang Y 

PROVIDER: S-EPMC8121330 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature