1 Introduction
The past decade has seen a remarkable progress in deep learning (DL) and their applications in recommender systems (RS). A variety of neural network models [1], [2], [3], [4], [5] with larger and deeper architectures are proposed to model user interaction behaviors from online systems. Among them, sequential recommendation models, such as the GRU4Rec [1], NextItNet [2] and SASRec [4] have become especially popular since they in general require neither much feature engineering nor explicit user embeddings when making recommendations. Despite the success, deep neural network models tend to fail in practice when their training data (i.e., user interactions) is insufficient. Such scenarios widely exist in practical RS, when a large number of new users enroll in but have fewer interactions. Fortunately, the interaction behaviors of cold-start users are likely to be accessible from other online systems. For example, a user in Amazon who has few purchase records might have hundreds of clicking interactions in YouTube. Such observed interaction feedback by YouTube could be a clue to infer her preference and make recommendations in Amazon.
In this paper, we assume data of both domains is available and put aside privacy concerns.
To this end, cross-domain recommendations (CDR) that transfer knowledge from a related source domain, have been proposed and become a popular way to tackle the recommendation problem of cold users.