With the development of modern internet techniques, Cross-Domain Recommendation
(CDR) systems have been widely exploited for tackling the data-sparsity problem …

User cold-start recommendation aims to provide accurate items for the newly joint users and
is a hot and challenging problem. Nowadays as people participant in different domains, how …

Cross-Domain Recommendation (CDR) has been popularly studied for solving the data
sparsity problem via leveraging rich knowledge from the auxiliary domain. Most of the …

Building high-quality recommender systems is challenging for new services and small
companies, because of their sparse interactions. Cross-domain recommendations (CDRs) …

In recent years, dual-target Cross-Domain Recommendation (CDR) has been proposed to
capture comprehensive user preferences in order to ultimately enhance the …

Although cross-domain recommender systems (CDRSs) are promising approaches to
solving the cold-start problem, most CDRSs require overlapped users, which significantly …

Cross-domain sequential recommenders (CSRs) are gaining considerable research
attention as they can capture user sequential preference by leveraging side information from …

Graph contrastive learning defines a contrastive task to pull similar instances close and push
dissimilar instances away. It learns discriminative node embeddings without supervised …

With the explosive growth of the number of online services and the items (ie, products) they
provide, it becomes extremely time-consuming for users to explore their interested products …

抄録 様々な機械学習モデルが実用的に利用される社会において, データの加工から学習・推論の
一連の流れである機械学習パイプライン (ML パイプライン) の重要性が増し, ML パイプライン管理 …