  Session-based recommendation predicts users' future interests from previous
interactions in a session. Despite the memorizing of historical samples, the
request of unlearning, i.e., to remove the effect of certain training samples,
also occurs for reasons such as user privacy or model fidelity. However,
existing studies on unlearning are not tailored for the session-based
recommendation. On the one hand, these approaches cannot achieve satisfying
unlearning effects due to the collaborative correlations and sequential
connections between the unlearning item and the remaining items in the session.
On the other hand, seldom work has conducted the research to verify the
unlearning effectiveness in the session-based recommendation scenario. In this
paper, we propose SRU, a session-based recommendation unlearning framework,
which enables high unlearning efficiency, accurate recommendation performance,
and improved unlearning effectiveness in session-based recommendation.
Specifically, we first partition the training sessions into separate sub-models
according to the similarity across the sessions, then we utilize an
attention-based aggregation layer to fuse the hidden states according to the
correlations between the session and the centroid of the data in the sub-model.
To improve the unlearning effectiveness, we further propose three extra data
deletion strategies, including collaborative extra deletion (CED), neighbor
extra deletion (NED), and random extra deletion (RED). Besides, we propose an
evaluation metric that measures whether the unlearning sample can be inferred
after the data deletion to verify the unlearning effectiveness. We implement
SRU with three representative session-based recommendation models and conduct
experiments on three benchmark datasets. Experimental results demonstrate the
effectiveness of our methods.
