Modeling Multi-Purpose Sessions for Next-Item Recommendations
via Mixture-Channel Purpose Routing Networks
Abstract
A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing
SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this
may not always be true in reality, and a session
may often consist of multiple subsets of items for
different purposes (e.g., breakfast and decoration).
Specifically, items (e.g., bread and milk) in a subset
have strong purpose-specific dependencies whereas
items (e.g., bread and vase) from different subsets
have much weaker or even no dependencies due
to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the
multi-purpose item subsets for more precisely representing a session. To address the shortcomings
in existing SBRSs, this model recommends more
diverse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose
routing networks (MCPRNs) with a purpose routing network to detect the purposes of each item
and assign them into the corresponding channels.
Moreover, a purpose-specific recurrent network is
devised to model the dependencies between items
within each channel for a specific purpose. The experimental results show the superiority of MCPRN
over the state-of-the-art methods in terms of both
recommendation accuracy and diversity