  To make Sequential Recommendation (SR) successful, recent works focus on
designing effective sequential encoders, fusing side information, and mining
extra positive self-supervision signals. The strategy of sampling negative
items at each time step is less explored. Due to the dynamics of users'
interests and model updates during training, considering randomly sampled items
from a user's non-interacted item set as negatives can be uninformative. As a
result, the model will inaccurately learn user preferences toward items.
Identifying informative negatives is challenging because informative negative
items are tied with both dynamically changed interests and model parameters
(and sampling process should also be efficient). To this end, we propose to
Generate Negative Samples (items) for SR (GenNi). A negative item is sampled at
each time step based on the current SR model's learned user preferences toward
items. An efficient implementation is proposed to further accelerate the
generation process, making it scalable to large-scale recommendation tasks.
Extensive experiments on four public datasets verify the importance of
providing high-quality negative samples for SR and demonstrate the
effectiveness and efficiency of GenNi.
