1 research outputs found
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations
Sequential Recommendation (SR) has been attracting a growing attention for
the superiority in modeling sequential information of user behaviors. We study
SR in a particularly challenging context, in which multiple individual users
share a single account (shared-account) and in which user behaviors are
available in multiple domains (cross-domain). These characteristics bring new
challenges on top of those of the traditional SR task. On the one hand, we need
to identify the behaviors by different user roles under the same account in
order to recommend the right item to the right user role at the right time. On
the other hand, we need to discriminate the behaviors from one domain that
might be helpful to improve recommendations in the other domains. In this work,
we formulate Shared-account Cross-domain Sequential Recommendation (SCSR) and
propose a parallel modeling network to address the two challenges above, namely
Parallel Split-Join Network (PSJNet). We present two variants of PSJNet,
PSJNet-I and PSJNet-II. PSJNet-I is a "Split-by-Join" framework where it splits
the mixed representations to get role-specific representations and join them to
get cross-domain representations at each timestamp simultaneously. PSJNet-II is
a "Split-and-Join" framework where it first splits role-specific
representations at each timestamp, and then the representations from all
timestamps and all roles are joined to get cross-domain representations. We use
two datasets to assess the effectiveness of PSJNet. The first dataset is a
simulated SCSR dataset obtained by randomly merging the Amazon logs from
different users in movie and book domains. The second dataset is a real-world
SCSR dataset built from smart TV watching logs of a commercial company. Our
experimental results demonstrate that PSJNet outperforms state-of-the-art
baselines in terms of MRR and Recall.Comment: Submitted to TOI