  Bundle recommendation aims to recommend the user a bundle of items as a
whole. Nevertheless, they usually neglect the diversity of the user's intents
on adopting items and fail to disentangle the user's intents in
representations. In the real scenario of bundle recommendation, a user's intent
may be naturally distributed in the different bundles of that user (Global
view), while a bundle may contain multiple intents of a user (Local view). Each
view has its advantages for intent disentangling: 1) From the global view, more
items are involved to present each intent, which can demonstrate the user's
preference under each intent more clearly. 2) From the local view, it can
reveal the association among items under each intent since items within the
same bundle are highly correlated to each other. To this end, we propose a
novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which
is capable of precisely and comprehensively capturing the diversity of the
user's intent and items' associations at the finer granularity. Specifically,
MIDGN disentangles the user's intents from two different perspectives,
respectively: 1) In the global level, MIDGN disentangles the user's intent
coupled with inter-bundle items; 2) In the Local level, MIDGN disentangles the
user's intent coupled with items within each bundle.
  Meanwhile, we compare the user's intents disentangled from different views
under the contrast learning framework to improve the learned intents. Extensive
experiments conducted on two benchmark datasets demonstrate that MIDGN
outperforms the state-of-the-art methods by over 10.7% and 26.8%, respectively.

Information Retrieval;Artificial Intelligence