  Session-based recommendation focuses on the prediction of user actions based
on anonymous sessions and is a necessary method in the lack of user historical
data. However, none of the existing session-based recommendation methods
explicitly takes the long-tail recommendation into consideration, which plays
an important role in improving the diversity of recommendation and producing
the serendipity. As the distribution of items with long-tail is prevalent in
session-based recommendation scenarios (e.g., e-commerce, music, and TV program
recommendations), more attention should be put on the long-tail session-based
recommendation. In this paper, we propose a novel network architecture, namely
TailNet, to improve long-tail recommendation performance, while maintaining
competitive accuracy performance compared with other methods. We start by
classifying items into short-head (popular) and long-tail (niche) items based
on click frequency. Then a novel is proposed and applied in TailNet to
determine user preference between two types of items, so as to softly adjust
and personalize recommendations. Extensive experiments on two real-world
datasets verify the superiority of our method compared with state-of-the-art
works.
