  Sequential recommendation (SR) is to accurately recommend a list of items for
a user based on her current accessed ones. While new-coming users continuously
arrive in the real world, one crucial task is to have inductive SR that can
produce embeddings of users and items without re-training. Given user-item
interactions can be extremely sparse, another critical task is to have
transferable SR that can transfer the knowledge derived from one domain with
rich data to another domain. In this work, we aim to present the holistic SR
that simultaneously accommodates conventional, inductive, and transferable
settings. We propose a novel deep learning-based model, Relational Temporal
Attentive Graph Neural Networks (RetaGNN), for holistic SR. The main idea of
RetaGNN is three-fold. First, to have inductive and transferable capabilities,
we train a relational attentive GNN on the local subgraph extracted from a
user-item pair, in which the learnable weight matrices are on various relations
among users, items, and attributes, rather than nodes or edges. Second,
long-term and short-term temporal patterns of user preferences are encoded by a
proposed sequential self-attention mechanism. Third, a relation-aware
regularization term is devised for better training of RetaGNN. Experiments
conducted on MovieLens, Instagram, and Book-Crossing datasets exhibit that
RetaGNN can outperform state-of-the-art methods under conventional, inductive,
and transferable settings. The derived attention weights also bring model
explainability.
