Abstract
The conventional methods for the next-item recommendation are generally based on RNN or onedimensional attention with time encoding. They
are either hard to preserve the long-term dependencies between different interactions, or hard to
capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers,
i.e., Item level Attention and Feature Level Selfattention, which are to pick out the most relevant
items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the
fine-grained user preferences to better support the
next-item recommendation. Extensive experiments
on two real-world datasets illustrate that DMRAN
can improve the efficiency and effectiveness of the
recommendation compared with the state-of-the-art
methods