In recent years,with the explosive growth of the rapid development of information technology,this explosive growth has promoted the emergence and development of cross-field recommendation systems.The design and implementation of cross-domain recommendation sys-tems face many challenges,including data heterogeneity and domain knowledge fusion.There-fore,the study of writing cross-field recommendation methods has become particularly impor-tant.These methods are designed to effectively integrate data and information from different domains while maintaining the efficiency and accuracy of recommender systems.In order to a-chieve this goal,researchers propose a variety of cross-domain recommendation methods,inclu-ding transfer-based learning methods,multi-task learning-based methods and other cross-domain recommendation methods.
transfer learningmulti-task learningshared representationmigration strategyme-ta-learningHybrid approachSubject-based model and knowledge-based image learning
NETL
NSTL
万方数据
