Which Matters Most in Making Fund Investment Decisions? A
  Multi-granularity Graph Disentangled Learning Framework
Chunjing Gan Binbin Hu Bo Huang Tianyu Zhao Yingru Lin Wenliang Zhong Zhiqiang Zhang Jun Zhou Chuan Shi
In this paper, we highlight that both conformity and risk preference matter
in making fund investment decisions beyond personal interest and seek to
jointly characterize these aspects in a disentangled manner. Consequently, we
develop a novel M ulti-granularity Graph Disentangled Learning framework named
MGDL to effectively perform intelligent matching of fund investment products.
Benefiting from the well-established fund graph and the attention module,
multi-granularity user representations are derived from historical behaviors to
separately express personal interest, conformity and risk preference in a
fine-grained way. To attain stronger disentangled representations with specific
semantics, MGDL explicitly involve two self-supervised signals, i.e., fund type
based contrasts and fund popularity. Extensive experiments in offline and
online environments verify the effectiveness of MGDL.