On the Effectiveness of Unlearning in Session-Based Recommendation

ACM International Conference on Web Search and Data Mining (WSDM), pp. 855–863

Abstract

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.

Authors 7

1. Xin Xin

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, Shandong, China

2. Liu Yang

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

3. Ziqi Zhao

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

4. Pengjie Ren

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

5. Zhumin Chen

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

6. Jun Ma

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

7. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands