  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.
