  Sequential recommendation involves automatically recommending the next item
to users based on their historical item sequence. While most prior research
employs RNN or transformer methods to glean information from the item
sequence-generating probabilities for each user-item pair and recommending the
top items, these approaches often overlook the challenge posed by spurious
relationships. This paper specifically addresses these spurious relations. We
introduce a novel sequential recommendation framework named Irl4Rec. This
framework harnesses invariant learning and employs a new objective that factors
in the relationship between spurious variables and adjustment variables during
model training. This approach aids in identifying spurious relations.
Comparative analyses reveal that our framework outperforms three typical
methods, underscoring the effectiveness of our model. Moreover, an ablation
study further demonstrates the critical role our model plays in detecting
spurious relations.
