  In this paper we develop a novel recommendation model that explicitly
incorporates time information. The model relies on an embedding layer and TSL
attention-like mechanism with inner products in different vector spaces, that
can be thought of as a modification of multi-headed attention. This mechanism
allows the model to efficiently treat sequences of user behavior of different
length. We study the properties of our state-of-the-art model on statistically
designed data set. Also, we show that it outperforms more complex models with
longer sequence length on the Taobao User Behavior dataset.
