Item Recommendation on Shared Accounts Through User Identification

Nowadays, people often share their subscription accounts, e.g. online content subscription accounts, among family members and friends. It is important to identify different users under one single account and then recommend specific items to decoupled individuals. In this paper, we propose the Projected Discriminant Attentive Embedding (PDAE) model and the Shared Account-aware Bayesian Personalized Ranking (SA-BPR) model for user identification and item recommendation, respectively. PDAE separates item consumption actions of each individual from mixed account history by learning the item representation that has both the user preference and user demographic information integrated; SA-BPR is a robust recommendation model based on a hierarchical ranking strategy where items from different sets are recommended with different levels of priorities. The experiments show that the proposed models generally outperform state-of-the-art approaches in both user identification and item recommendation.