π-Net: A Parallel Information-sharing Network for Shared-account Cross-domain Sequential Recommendations

Sequential Recommendation (SR) is the task of recommending the next item based on a sequence of recorded 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 different user behaviors under the same account in order to recommend the right item to the right user 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. We formulate the Shared-account Cross-domain Sequential Recommendation (SCSR) task as a parallel sequential recommendation problem. We propose a Parallel Information-sharing Network (π-Net) to simultaneously generate recommendations for two domains where user behaviors on two domains are synchronously shared at each timestamp. π-Net contains two core units: a shared account filter unit (SFU) and a cross-domain transfer unit (CTU). The SFU is used to address the challenge raised by shared accounts; it learns user-specific representations, and uses a gating mechanism to filter out information of some users that might be useful for another domain. The CTU is used to address the challenge raised by the cross-domain setting; it adaptively combines the information from the SFU at each timestamp and then transfers it to another domain. After that, π-Net estimates recommendation scores for each item in two domains by integrating information from both domains. To assess the effectiveness of π-Net, we construct a new dataset HVIDEO from real-world smart TV watching logs. To the best of our knowledge, this is the first dataset with both shared-account and cross-domain characteristics. We release HVIDEO to facilitate future research. Our experimental results demonstrate that π-Net outperforms state-of-the-art baselines in terms of MRR and Recall.