MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain
  Recommendation
Xin Su Yao Zhou Zifei Shan Qian Chen
It is a long-standing challenge in modern recommender systems to effectively
make recommendations for new users, namely the cold-start problem. Cross-Domain
Recommendation (CDR) has been proposed to address this challenge, but current
ways to represent users' interests across systems are still severely limited.
We introduce Personal Knowledge Graph (PKG) as a domain-invariant interest
representation, and propose a novel CDR paradigm named MeKB-Rec. We first link
users and entities in a knowledge base to construct a PKG of users' interests,
named MeKB. Then we learn a semantic representation of MeKB for the
cross-domain recommendation. To efficiently utilize limited training data in
CDR, MeKB-Rec employs Pretrained Language Models to inject world knowledge into
understanding users' interests. Beyond most existing systems, our approach
builds a semantic mapping across domains which breaks the requirement for
in-domain user behaviors, enabling zero-shot recommendations for new users in a
low-resource domain. We experiment MeKB-Rec on well-established public CDR
datasets, and demonstrate that the new formulation % is more powerful than
previous approaches, achieves a new state-of-the-art that significantly
improves HR@10 and NDCG@10 metrics over best previous approaches by 24\%--91\%,
with a 105\% improvement for HR@10 of zero-shot users with no behavior in the
target domain. We deploy MeKB-Rec in WeiXin recommendation scenarios and
achieve significant gains in core online metrics. MeKB-Rec is now serving
hundreds of millions of users in real-world products.