  Recently, self-attention based models have achieved state-of-the-art
performance in sequential recommendation task. Following the custom from
language processing, most of these models rely on a simple positional embedding
to exploit the sequential nature of the user's history. However, there are some
limitations regarding the current approaches. First, sequential recommendation
is different from language processing in that timestamp information is
available. Previous models have not made good use of it to extract additional
contextual information. Second, using a simple embedding scheme can lead to
information bottleneck since the same embedding has to represent all possible
contextual biases. Third, since previous models use the same positional
embedding in each attention head, they can wastefully learn overlapping
patterns. To address these limitations, we propose MEANTIME (MixturE of
AtteNTIon mechanisms with Multi-temporal Embeddings) which employs multiple
types of temporal embeddings designed to capture various patterns from the
user's behavior sequence, and an attention structure that fully leverages such
diversity. Experiments on real-world data show that our proposed method
outperforms current state-of-the-art sequential recommendation methods, and we
provide an extensive ablation study to analyze how the model gains from the
diverse positional information.
