  Session-based recommendation intends to predict next purchased items based on
anonymous behavior sequences. Numerous economic studies have revealed that item
price is a key factor influencing user purchase decisions. Unfortunately,
existing methods for session-based recommendation only aim at capturing user
interest preference, while ignoring user price preference. Actually, there are
primarily two challenges preventing us from accessing price preference.
Firstly, the price preference is highly associated to various item features
(i.e., category and brand), which asks us to mine price preference from
heterogeneous information. Secondly, price preference and interest preference
are interdependent and collectively determine user choice, necessitating that
we jointly consider both price and interest preference for intent modeling. To
handle above challenges, we propose a novel approach Bi-Preference Learning
Heterogeneous Hypergraph Networks (BiPNet) for session-based recommendation.
Specifically, the customized heterogeneous hypergraph networks with a
triple-level convolution are devised to capture user price and interest
preference from heterogeneous features of items. Besides, we develop a
Bi-Preference Learning schema to explore mutual relations between price and
interest preference and collectively learn these two preferences under the
multi-task learning architecture. Extensive experiments on multiple public
datasets confirm the superiority of BiPNet over competitive baselines.
Additional research also supports the notion that the price is crucial for the
task.
