  Conversational recommendation systems (CRS) aim to interactively acquire user
preferences and accordingly recommend items to users. Accurately learning the
dynamic user preferences is of crucial importance for CRS. Previous works learn
the user preferences with pairwise relations from the interactive conversation
and item knowledge, while largely ignoring the fact that factors for a
relationship in CRS are multiplex. Specifically, the user likes/dislikes the
items that satisfy some attributes (Like/Dislike view). Moreover social
influence is another important factor that affects user preference towards the
item (Social view), while is largely ignored by previous works in CRS. The user
preferences from these three views are inherently different but also correlated
as a whole. The user preferences from the same views should be more similar
than that from different views. The user preferences from Like View should be
similar to Social View while different from Dislike View. To this end, we
propose a novel model, namely Multi-view Hypergraph Contrastive Policy Learning
(MHCPL). Specifically, MHCPL timely chooses useful social information according
to the interactive history and builds a dynamic hypergraph with three types of
multiplex relations from different views. The multiplex relations in each view
are successively connected according to their generation order.
