  Sequential recommendation techniques provide users with product
recommendations fitting their current preferences by handling dynamic user
preferences over time. Previous studies have focused on modeling sequential
dynamics without much regard to which of the best-selling products (i.e., head
items) or niche products (i.e., tail items) should be recommended. We
scrutinize the structural reason for why tail items are barely served in the
current sequential recommendation model, which consists of an item-embedding
layer, a sequence-modeling layer, and a recommendation layer. Well-designed
sequence-modeling and recommendation layers are expected to naturally learn
suitable item embeddings. However, tail items are likely to fall short of this
expectation because the current model structure is not suitable for learning
high-quality embeddings with insufficient data. Thus, tail items are rarely
recommended. To eliminate this issue, we propose a framework called CITIES,
which aims to enhance the quality of the tail-item embeddings by training an
embedding-inference function using multiple contextual head items so that the
recommendation performance improves for not only the tail items but also for
the head items. Moreover, our framework can infer new-item embeddings without
an additional learning process. Extensive experiments on two real-world
datasets show that applying CITIES to the state-of-the-art methods improves
recommendation performance for both tail and head items. We conduct an
additional experiment to verify that CITIES can infer suitable new-item
embeddings as well.
