  Incorporating knowledge graphs (KGs) as side information in recommendation
has recently attracted considerable attention. Despite the success in general
recommendation scenarios, prior methods may fall short of performance
satisfaction for the cold-start problem in which users are associated with very
limited interactive information. Since the conventional methods rely on
exploring the interaction topology, they may however fail to capture sufficient
information in cold-start scenarios. To mitigate the problem, we propose a
novel Knowledge-aware Neural Networks with Personalized Feature Referencing
Mechanism, namely KPER. Different from most prior methods which simply enrich
the targets' semantics from KGs, e.g., product attributes, KPER utilizes the
KGs as a "semantic bridge" to extract feature references for cold-start users
or items. Specifically, given cold-start targets, KPER first probes
semantically relevant but not necessarily structurally close users or items as
adaptive seeds for referencing features. Then a Gated Information Aggregation
module is introduced to learn the combinatorial latent features for cold-start
users and items. Our extensive experiments over four real-world datasets show
that, KPER consistently outperforms all competing methods in cold-start
scenarios, whilst maintaining superiority in general scenarios without
compromising overall performance, e.g., by achieving 0.81%-16.08% and
1.01%-14.49% performance improvement across all datasets in Top-10
recommendation.
