Parallel Split-Join Networks for Shared Account Cross-Domain Sequential Recommendations

Sequential recommendation is a task in which one models and uses sequential information about user behavior for recommendation purposes. We study sequential recommendation in a context in which multiple individual users share a single account (i.e., they have a shared account) and in which user behavior is available in multiple domains (i.e., recommendations are cross-domain). These two characteristics bring new challenges on top of those of the traditional sequential recommendation task. First, we need to identify the behavior associated with different users and different user roles under the same account in order to recommend the right item to the right user role at the right time. Second, we need to identify behavior in one domain that might be helpful to improve recommendations in other domains. We study <italic>shared account cross-domain sequential recommendation</italic> and propose a <bold>p</bold>arallel <bold>s</bold>plit-<bold>j</bold>oin <bold>Net</bold>work (Parallel Split-Join Network (PSJNet)), a parallel modeling network to address the two challenges above. We use &#x201C;split&#x201D; to address the challenge raised by shared accounts; PSJNet learns role-specific representations and uses a gating mechanism to filter out, from mixed user behavior, information of user roles that might be useful for another domain. In addition, &#x201C;join&#x201D; is used to address the challenge raised by the cross-domain setting; PSJNet learns cross-domain representations by combining the information from &#x201C;split&#x201D; and then transforms it to another domain. We present two variants of PSJNet: PSJNet-I and PSJNet-II. PSJNet-I is a &#x201C;split-by-join&#x201D; framework that splits the mixed representations to get role-specific representations and joins them to obtain cross-domain representations at each timestamp simultaneously. PSJNet-II is a &#x201C;split-and-join&#x201D; framework that first splits role-specific representations at each timestamp, and then the representations from all timestamps and all roles are joined to obtain cross-domain representations. We concatenate the in-domain and cross-domain representations to compute a recommendation score for each item. Both PSJNet-I and PSJNet-II can simultaneously generate recommendations for two domains where user behavior in two domains is synchronously shared at each timestamp. We use two datasets to assess the effectiveness of PSJNet. The first dataset is a simulated shared account cross-domain sequential recommendation dataset obtained by randomly merging the Amazon logs from different users in the movie and book domains. The second dataset is a real-world shared account cross-domain sequential recommendation dataset built from smart TV watching logs of a commercial organization. Our experimental results demonstrate that PSJNet outperforms state-of-the-art sequential recommendation baselines in terms of MRR and Recall.