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Sequential recommendation is a task in which one models and uses sequentialinformation about user behavior for recommendation purposes. We studysequential recommendation in a particularly challenging context, in whichmultiple individual users share asingle account (i.e., they have a sharedaccount) and in which user behavior is available in multiple domains (i.e.,recommendations are cross-domain). These two characteristics bring newchallenges on top of those of the traditional sequential recommendation task.First, we need to identify the behavior associated with different users anddifferent user roles under the same account in order to recommend the rightitem to the right user role at the right time. Second, we need to identifybehavior in one domain that might be helpful to improve recommendations inother domains. In this work, we study shared account cross-domain sequentialrecommendation and propose Parallel Split-Join Network (PSJNet), a parallelmodeling network to address the two challenges above. We present two variantsof PSJNet, PSJNet-I and PSJNet-II. PSJNet-I is a "split-by-join" framework thatsplits the mixed representations to get role-specific representations and joinsthem to obtain cross-domain representations at each timestamp simultaneously.PSJNet-II is a "split-and-join" framework that first splits role-specificrepresentations at each timestamp, and then the representations from alltimestamps and all roles are joined to obtain cross-domain representations. Weuse two datasets to assess the effectiveness of PSJNet. Our experimentalresults demonstrate that PSJNet outperforms state-of-the-art sequentialrecommendation baselines in terms of MRR and Recall.

Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations